Med-PRM: Medical Reasoning Models with Stepwise, Guideline-verified
  Process Rewards
Large language models have shown promise in clinical decision making, but
current approaches struggle to localize and correct errors at specific steps of
the reasoning process. This limitation is critical in medicine, where
identifying and addressing reasoning errors is essential for accurate diagnosis
and effective patient care. We introduce Med-PRM, a process reward modeling
framework that leverages retrieval-augmented generation to verify each
reasoning step against established medical knowledge bases. By verifying
intermediate reasoning steps with evidence retrieved from clinical guidelines
and literature, our model can precisely assess the reasoning quality in a
fine-grained manner. Evaluations on five medical QA benchmarks and two
open-ended diagnostic tasks demonstrate that Med-PRM achieves state-of-the-art
performance, with improving the performance of base models by up to 13.50%
using Med-PRM. Moreover, we demonstrate the generality of Med-PRM by
integrating it in a plug-and-play fashion with strong policy models such as
Meerkat, achieving over 80\% accuracy on MedQA for the first time using
small-scale models of 8 billion parameters. Our code and data are available at:
https://med-prm.github.io/
684ffd121d9b438aa3957aa4
Jaehoon Yun
684ffd121d9b438aa3957aa5
Jiwoong Sohn
claimed_verified
2025-06-16T12:56:03.283Z
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Jiwoong Sohn
user
jw-sohn
684ffd121d9b438aa3957aa6
Jungwoo Park
claimed_verified
2025-06-24T16:25:15.905Z
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Jungwoo Park
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affjljoo3581
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Hyunjae Kim
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Xiangru Tang
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Yanjun Shao
684ffd121d9b438aa3957aaa
Yonghoe Koo
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Minhyeok Ko
684ffd121d9b438aa3957aac
Qingyu Chen
684ffd121d9b438aa3957aad
Mark Gerstein
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Michael Moor
claimed_verified
2025-06-17T21:13:25.918Z
6438d1d843d932c462404500
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Michael Moor
user
mdmoor
684ffd121d9b438aa3957aaf
Jaewoo Kang
Med-PRM: Medical Reasoning Models with Stepwise, Guideline-verified
  Process Rewards
Large language models have shown promise in clinical decision making, but
current approaches struggle to localize and correct errors at specific steps of
the reasoning process. This limitation is critical in medicine, where
identifying and addressing reasoning errors is essential for accurate diagnosis
and effective patient care. We introduce Med-PRM, a process reward modeling
framework that leverages retrieval-augmented generation to verify each
reasoning step against established medical knowledge bases. By verifying
intermediate reasoning steps with evidence retrieved from clinical guidelines
and literature, our model can precisely assess the reasoning quality in a
fine-grained manner. Evaluations on five medical QA benchmarks and two
open-ended diagnostic tasks demonstrate that Med-PRM achieves state-of-the-art
performance, with improving the performance of base models by up to 13.50%
using Med-PRM. Moreover, we demonstrate the generality of Med-PRM by
integrating it in a plug-and-play fashion with strong policy models such as
Meerkat, achieving over 80\% accuracy on MedQA for the first time using
small-scale models of 8 billion parameters. Our code and data are available at:
https://med-prm.github.io/
Med-PRM enhances clinical decision making by verifying reasoning steps against medical knowledge bases, achieving state-of-the-art performance in medical QA benchmarks with improved accuracy.
retrieval-augmented generation process reward modeling Med-PRM intermediate reasoning steps clinical guidelines literature reasoning quality fine-grained assessment MedQA Meerkat accuracy