MedAgentGym: Training LLM Agents for Code-Based Medical Reasoning at
  Scale
Authors: name: Ran Xu, name: Yuchen Zhuang, name: Yishan Zhong, name: Yue Yu, name: Xiangru Tang, name: Hang Wu, name: May D. Wang, name: Peifeng Ruan, name: Donghan Yang, name: Tao Wang, name: Guanghua Xiao, name: Carl Yang, name: Yang Xie, name: Wenqi Shi

We introduce MedAgentGYM, the first publicly available training environment
designed to enhance coding-based medical reasoning capabilities in large
language model (LLM) agents. MedAgentGYM comprises 72,413 task instances across
129 categories derived from authentic real-world biomedical scenarios. Tasks
are encapsulated within executable coding environments, each featuring detailed
task descriptions, interactive feedback mechanisms, verifiable ground-truth
annotations, and scalable training trajectory generation. Extensive
benchmarking of over 30 LLMs reveals a notable performance disparity between
commercial API-based models and open-source counterparts. Leveraging
MedAgentGYM, Med-Copilot-7B achieves substantial performance gains through
supervised fine-tuning (+36.44%) and continued reinforcement learning
(+42.47%), emerging as an affordable and privacy-preserving alternative
competitive with gpt-4o. By offering both a comprehensive benchmark and
accessible, expandable training resources within unified execution
environments, MedAgentGYM delivers an integrated platform to develop LLM-based
coding assistants for advanced biomedical research and practice.