初始化项目，由ModelHub XC社区提供模型

Model: Gen-Verse/ReasonFlux-PRM-1.5B
Source: Original Platform

README.md

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+---
+license: mit
+library_name: transformers
+---
+
+
+# ReasonFlux-PRM
+
+[Code](https://github.com/Gen-Verse/ReasonFlux) | [Paper](https://arxiv.org/abs/2506.18896)
+
+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
+
+```bash
+@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}
+}
+```