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

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.