| Intended Application & Domain: |
Generative reward model, suitable for LLM-as-a-Judge and Reinforcement Learning from Human Feedback. |
| Model Type: |
Qwen3-235B-A22B MoE |
| Intended User: |
Developers designing AI Agent systems, chatbots, RAG systems, and other AI-powered applications. |
| Output: |
Text (String, One-Dimensional sequences). |
| Describe how the model works: |
Given a conversation history, a user request, and two candidate responses, the model generates a reasoning trace then an individual helpfulness score for each response and a ranking score. |
| Name the adversely impacted groups this has been tested to deliver comparable outcomes regardless of: |
N/A |
| Technical Limitations: |
The model's max sequence length is 128K tokens. Longer text inputs should be truncated. The model may show decreased accuracy for harder prompts when configured to skip intermediate reasoning traces. |
| Verified to have met prescribed NVIDIA quality standards: |
Yes |
| Performance Metrics: |
Accuracy, Throughput, and Latency. |
| Potential Known Risks: |
The model was trained on data that contains toxic language and societal biases originally crawled from the internet. Therefore, the model may amplify those biases and return toxic responses especially when prompted with toxic prompts. The model may generate answers that may be inaccurate, omit key information, or include irrelevant or redundant text producing socially unacceptable or undesirable text, even if the prompt itself does not include anything explicitly offensive. |
| Licensing & Terms of Use: |
Apache 2.0 |
