Baichuan-M2: Scaling Medical Capability with Large Verifier System
Authors: name: Baichuan-M2 Team; name: Chengfeng Dou; name: Chong Liu; name: Fan Yang; name: Fei Li; name: Jiyuan Jia; name: Mingyang Chen; name: Qiang Ju; name: Shuai Wang; name: Shunya Dang; name: Tianpeng Li; name: Xiangrong Zeng; name: Yijie Zhou; name: Chenzheng Zhu; name: Da Pan; name: Fei Deng; name: Guangwei Ai; name: Guosheng Dong; name: Hongda Zhang; name: Jinyang Tai; name: Jixiang Hong; name: Kai Lu; name: Linzhuang Sun; name: Peidong Guo; name: Qian Ma; name: Rihui Xin; name: Shihui Yang; name: Shusen Zhang; name: Yichuan Mo; name: Zheng Liang; name: Zhishou Zhang; name: Hengfu Cui; name: Zuyi Zhu; name: Xiaochuan Wang
Published: 2025-09-02 11:23:35

Abstract: As large language models (LLMs) advance in conversational and reasoning
capabilities, their practical application in healthcare has become a critical
research focus. However, there is a notable gap between the performance of
medical LLMs on static benchmarks such as USMLE and their utility in real-world
clinical decision-making. This discrepancy arises because traditional exams
fail to capture the dynamic, interactive nature of medical consultations. To
address this challenge, we introduce a novel dynamic verification framework
that moves beyond static answer verifier, establishing a large-scale,
high-fidelity interactive reinforcement learning system. Our framework
comprises two key components: a Patient Simulator that creates realistic
clinical environments using de-identified medical records, and a Clinical
Rubrics Generator that dynamically produces multi-dimensional evaluation
metrics. Building on this foundation, we develop Baichuan-M2, a 32B-parameter
medical augmented reasoning model trained through a multi-stage reinforcement
learning strategy with an improved Group Relative Policy Optimization (GRPO)
algorithm. Evaluated on HealthBench, Baichuan-M2 outperforms all other
open-source models and most advanced closed-source counterparts, achieving a
score above 32 on the challenging HealthBench Hard benchmark-previously
exceeded only by GPT-5. Our work demonstrates that robust dynamic verifier
system is essential for aligning LLM capabilities with practical clinical
applications, establishing a new Pareto front in the performance-parameter
trade-off for medical AI deployment.