Med-U1: Incentivizing Unified Medical Reasoning in LLMs via Large-scale Reinforcement Learning

• URL: http://arxiv.org/abs/2506.12307v2
• Date: Fri, 20 Jun 2025 01:43:46 GMT
• Title: Med-U1: Incentivizing Unified Medical Reasoning in LLMs via Large-scale Reinforcement Learning
• Authors: Xiaotian Zhang, Yuan Wang, Zhaopeng Feng, Ruizhe Chen, Zhijie Zhou, Yan Zhang, Hongxia Xu, Jian Wu, Zuozhu Liu,
• Abstract summary: We present Med-U1, a unified framework for robust reasoning across medical Question-Answering (QA) tasks.<n>With multi-objective reward optimization, Med-U1 directs LLMs to produce concise and verifiable reasoning chains.<n> Empirical results reveal that Med-U1 significantly improves performance across multiple challenging Med-QA benchmarks.
• Score: 20.878972841860975
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Medical Question-Answering (QA) encompasses a broad spectrum of tasks, including multiple choice questions (MCQ), open-ended text generation, and complex computational reasoning. Despite this variety, a unified framework for delivering high-quality medical QA has yet to emerge. Although recent progress in reasoning-augmented large language models (LLMs) has shown promise, their ability to achieve comprehensive medical understanding is still largely unexplored. In this paper, we present Med-U1, a unified framework for robust reasoning across medical QA tasks with diverse output formats, ranging from MCQs to complex generation and computation tasks. Med-U1 employs pure large-scale reinforcement learning with mixed rule-based binary reward functions, incorporating a length penalty to manage output verbosity. With multi-objective reward optimization, Med-U1 directs LLMs to produce concise and verifiable reasoning chains. Empirical results reveal that Med-U1 significantly improves performance across multiple challenging Med-QA benchmarks, surpassing even larger specialized and proprietary models. Furthermore, Med-U1 demonstrates robust generalization to out-of-distribution (OOD) tasks. Extensive analysis presents insights into training strategies, reasoning chain length control, and reward design for medical LLMs. Our code is available here.

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