ProMed: Shapley Information Gain Guided Reinforcement Learning for Proactive Medical LLMs

Hongxin Ding; Baixiang Huang; Yue Fang; Weibin Liao; Xinke Jiang; Jinyang Zhang; Yinghao Zhu; Zheng Li; Liantao Ma; Junfeng Zhao; Yasha Wang

arXiv · 2508.13514

ProMed: Shapley Information Gain Guided Reinforcement Learning for Proactive Medical LLMs

Hongxin Ding·Baixiang Huang·Yue Fang·Weibin Liao·Xinke Jiang·Jinyang Zhang·Yinghao Zhu·Zheng Li·Liantao Ma·Junfeng Zhao·Yasha Wang

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Abstract

Interactive medical questioning is essential in clinical consultations, where physicians must actively gather necessary patient information. Yet existing medical Large Language Models (LLMs) predominantly follow a reactive paradigm, risking diagnostic errors by answering before seeking sufficient details. To bridge this gap, we propose ProMed, a reinforcement learning framework that transitions LLMs toward a proactive paradigm, enabling them to ask clinically valuable questions before decision-making. Central to ProMed is the Shapley Information Gain (SIG) reward, which quantifies a question's clinical utility as the amount of newly acquired information, while considering its contextual importance via Shapley values. We integrate SIG into a two-stage training pipeline: (1) SIG-Guided Model Initialization uses Monte Carlo Tree Search to construct high-reward interaction trajectories for supervision, and (2) SIG-Augmented Policy Optimization, with a novel SIG-guided Reward Distribution Mechanism that prioritizes informative questions for fine-grained optimization. Experiments on partial-information medical benchmarks show that ProMed significantly outperforms state-of-the-art methods by 6.29% on average and delivers a 54.45% gain over the reactive paradigm, and generalizes robustly to out-of-domain cases. Our codes are available at https://github.com/hxxding/ProMed.

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Hongxin Ding, Baixiang Huang, Yue Fang, Weibin Liao, Xinke Jiang, Jinyang Zhang, Yinghao Zhu, Zheng Li, Liantao Ma, Junfeng Zhao, Yasha Wang. 2026-06-22. ProMed: Shapley Information Gain Guided Reinforcement Learning for Proactive Medical LLMs. https://arxiv.org/abs/2508.13514

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