# 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.

Model	Type	Size	Capabilities	Use Cases	Download
ReasonFlux-PRM	PRM	7B	• Trajectory-aware scoring • Online/Offline supervision • Dense process rewards	Data selection, RL training, Test-time scaling	🤗 7B
ReasonFlux-PRM	PRM	1.5B	• Lightweight scoring • Efficient inference • Edge deployment	Resource-constrained applications	🤗 1.5B
ReasonFlux-PRM-Qwen-2.5	End-to-End Trained Policy Model	7B	• Long CoT reasoning • Solving complex tasks and problems	Math and Science Reasoning	🤗 7B

>*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} } ```