@inproceedings{yun-etal-2025-med,
    title = "{M}ed-{PRM}: Medical Reasoning Models with Stepwise, Guideline-verified Process Rewards",
    author = "Yun, Jaehoon  and
      Sohn, Jiwoong  and
      Park, Jungwoo  and
      Kim, Hyunjae  and
      Tang, Xiangru  and
      Shao, Daniel  and
      Koo, Yong Hoe  and
      Minhyeok, Ko  and
      Chen, Qingyu  and
      Gerstein, Mark  and
      Moor, Michael  and
      Kang, Jaewoo",
    editor = "Christodoulopoulos, Christos  and
      Chakraborty, Tanmoy  and
      Rose, Carolyn  and
      Peng, Violet",
    booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2025.emnlp-main.837/",
    pages = "16565--16582",
    ISBN = "979-8-89176-332-6",
    abstract = "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."
}