ReasonFlux-PRM: Trajectory-Aware PRMs for Long Chain-of-Thought
  Reasoning in LLMs
Process Reward Models (PRMs) have recently emerged as a powerful framework
for supervising intermediate reasoning steps in large language models (LLMs).
Previous PRMs are primarily trained on model final output responses and
struggle to evaluate intermediate thinking trajectories robustly, especially in
the emerging setting of trajectory-response outputs generated by frontier
reasoning models like Deepseek-R1. In this work, we introduce ReasonFlux-PRM, a
novel trajectory-aware 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. We adapt
ReasonFlux-PRM to support reward supervision under both offline and online
settings, including (i) selecting high-quality model distillation data for
downstream supervised fine-tuning of smaller models, (ii) providing dense
process-level rewards for policy optimization during reinforcement learning,
and (iii) enabling reward-guided Best-of-N test-time scaling. Empirical results
on challenging downstream benchmarks such as AIME, MATH500, and GPQA-Diamond
demonstrate that ReasonFlux-PRM-7B selects higher quality data than strong PRMs
(e.g., Qwen2.5-Math-PRM-72B) and human-curated baselines. Furthermore, our
derived ReasonFlux-PRM-7B yields consistent performance improvements, achieving
average gains of 12.1% in supervised fine-tuning, 4.5% in reinforcement
learning, and 6.3% in test-time scaling. We also release our efficient
ReasonFlux-PRM-1.5B for resource-constrained applications and edge deployment.
Projects: https://github.com/Gen-Verse/ReasonFlux
685a02790e4ad7e2197584b2
Jiaru Zou
685a02790e4ad7e2197584b3
Ling Yang
claimed_verified
2025-09-09T13:51:56.315Z
64fde4e252e82dd432b74ce9
https://cdn-avatars.huggingface.co/v1/production/uploads/64fde4e252e82dd432b74ce9/-CQZbBP7FsPPyawYrsi4z.jpeg
Ling Yang
user
Lingaaaaaaa
685a02790e4ad7e2197584b4
Jingwen Gu
685a02790e4ad7e2197584b5
Jiahao Qiu
admin_assigned
2025-06-24T12:53:36.246Z
65271df5abd7795aaa1fd86e
/avatars/99e1aabaefcf5e7f438817375cd1ddef.svg
Jiahao Qiu
user
jiahaoq
685a02790e4ad7e2197584b6
Ke Shen
claimed_verified
2025-06-30T14:03:42.168Z
645604eebabbbbd3486dc615
/avatars/17a5ca8274e2bfc8f183a4af9878a930.svg
shenke
user
shenke18
685a02790e4ad7e2197584b7
Jingrui He
685a02790e4ad7e2197584b8
Mengdi Wang
ReasonFlux-PRM: Trajectory-Aware PRMs for Long Chain-of-Thought
  Reasoning in LLMs
Process Reward Models (PRMs) have recently emerged as a powerful framework
for supervising intermediate reasoning steps in large language models (LLMs).
Previous PRMs are primarily trained on model final output responses and
struggle to evaluate intermediate thinking trajectories robustly, especially in
the emerging setting of trajectory-response outputs generated by frontier
reasoning models like Deepseek-R1. In this work, we introduce ReasonFlux-PRM, a
novel trajectory-aware 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. We adapt
ReasonFlux-PRM to support reward supervision under both offline and online
settings, including (i) selecting high-quality model distillation data for
downstream supervised fine-tuning of smaller models, (ii) providing dense
process-level rewards for policy optimization during reinforcement learning,
and (iii) enabling reward-guided Best-of-N test-time scaling. Empirical results
on challenging downstream benchmarks such as AIME, MATH500, and GPQA-Diamond
demonstrate that ReasonFlux-PRM-7B selects higher quality data than strong PRMs
(e.g., Qwen2.5-Math-PRM-72B) and human-curated baselines. Furthermore, our
derived ReasonFlux-PRM-7B yields consistent performance improvements, achieving
average gains of 12.1% in supervised fine-tuning, 4.5% in reinforcement
learning, and 6.3% in test-time scaling. We also release our efficient
ReasonFlux-PRM-1.5B for resource-constrained applications and edge deployment.
Projects: https://github.com/Gen-Verse/ReasonFlux
ReasonFlux-PRM, a novel trajectory-aware Process Reward Model, evaluates reasoning traces with step-level and trajectory-level supervision, enhancing performance in model distillation, reinforcement learning, and test-time scaling.
Process Reward Models trajectory-aware PRM trajectory-response outputs step-level supervision trajectory-level supervision chain-of-thought data model distillation policy optimization reinforcement learning Best-of-N test-time scaling AIME MATH500 GPQA-Diamond