MedAgentGym: Training LLM Agents for Code-Based Medical Reasoning at
  Scale
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.
6842454fbdc448822b2f1c03
Ran Xu
6842454fbdc448822b2f1c04
Yuchen Zhuang
6842454fbdc448822b2f1c05
Yishan Zhong
6842454fbdc448822b2f1c06
Yue Yu
6842454fbdc448822b2f1c07
Xiangru Tang
6842454fbdc448822b2f1c08
Hang Wu
6842454fbdc448822b2f1c09
May D. Wang
6842454fbdc448822b2f1c0a
Peifeng Ruan
6842454fbdc448822b2f1c0b
Donghan Yang
6842454fbdc448822b2f1c0c
Tao Wang
6842454fbdc448822b2f1c0d
Guanghua Xiao
6842454fbdc448822b2f1c0e
Carl Yang
6842454fbdc448822b2f1c0f
Yang Xie
6842454fbdc448822b2f1c10
Wenqi Shi
claimed_verified
2025-06-06T07:41:39.845Z
65cae89119683f9817c049ea
https://cdn-avatars.huggingface.co/v1/production/uploads/65cae89119683f9817c049ea/A0XxjmaJldu28JhFvWmpP.jpeg
Wenqi Shi
user
wshi83
MedAgentGym: Training LLM Agents for Code-Based Medical Reasoning at
  Scale
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.
MedAgentGYM, a training environment for coding-based medical reasoning in LLMs, enhances performance through supervised fine-tuning and reinforcement learning, providing a benchmark and expandable resource.
large language model MedAgentGYM task instances biomedical scenarios coding environments task descriptions interactive feedback ground-truth annotations training trajectories LLMs supervised fine-tuning reinforcement learning Med-Copilot-7B gpt-4o coding assistants biomedical research