ReasonFlux-PRM-Qwen-2.5-7B

ReasonFlux-PRM

Code | Paper

We introduce ReasonFlux-PRM, a trajectory-aware process reward model (PRM) explicitly designed to evaluate the trajectory-response type of reasoning traces. ReasonFlux-PRM incorporates both step-level and trajectory-level supervision, enabling fine-grained reward assignment aligned with structured chain-of-thought data. ReasonFlux-PRM is able to support both offline and online reward supervision, by selecting high-quality training data for model distillation, providing dense process-level rewards for policy optimization during reinforcement learning, and enabling reward-guided test-time scaling.

<table> <tr> <th>Model</th> <th>Type</th> <th>Size</th> <th>Capabilities</th> <th>Use Cases</th> <th>Download</th> </tr> <tr> <td><strong>ReasonFlux-PRM</strong></td> <td>PRM</td> <td>7B</td> <td>• Trajectory-aware scoring<br/>• Online/Offline supervision<br/>• Dense process rewards</td> <td>Data selection, RL training, Test-time scaling</td> <td><a href="https://huggingface.co/Gen-Verse/ReasonFlux-PRM-7B">🤗 7B</a></td> </tr> <tr> <td><strong>ReasonFlux-PRM</strong></td> <td>PRM</td> <td>1.5B</td> <td>• Lightweight scoring<br/>• Efficient inference<br/>• Edge deployment</td> <td>Resource-constrained applications</td> <td><a href="https://huggingface.co/Gen-Verse/ReasonFlux-PRM-1.5B">🤗 1.5B</a></td> </tr> </tr> <tr> <td><strong>ReasonFlux-PRM-Qwen-2.5</strong></td> <td>End-to-End Trained Policy Model</td> <td>7B</td> <td>• Long CoT reasoning <br/>• Solving complex tasks and problems</td> <td>Math and Science Reasoning</td> <td><a href="https://huggingface.co/Gen-Verse/ReasonFlux-PRM-Qwen-2.5-7B">🤗 7B</a></td> </tr> </table>

Note: We obtain ReasonFlux-PRM-Qwen-2.5-7B through an end-to-end training process, first applying SFT on 1k Trajectory–Response pairs selected by ReasonFlux-PRM-7B, followed by RL training with ReasonFlux-PRM-7B integrated GRPO.

Citation

@article{zou2025reasonfluxprm,
  title={ReasonFlux-PRM: Trajectory-Aware PRMs for Long Chain-of-Thought Reasoning in LLMs},
  author={Zou, Jiaru and Yang, Ling and Gu, Jingwen and Qiu, Jiahao and Shen, Ke and He, Jingrui and Wang, Mengdi},
  journal={arXiv preprint arXiv:2506.18896},
  year={2025}
}