Med-PRM: Medical Reasoning Models with Stepwise, Guideline-verified
  Process Rewards
Authors: name: Jaehoon Yun, name: Jiwoong Sohn, name: Jungwoo Park, name: Hyunjae Kim, name: Xiangru Tang, name: Yanjun Shao, name: Yonghoe Koo, name: Minhyeok Ko, name: Qingyu Chen, name: Mark Gerstein, name: Michael Moor, name: Jaewoo Kang

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/