[2510.04284] Doctor-R1: Mastering Clinical Inquiry with Experiential Agentic Reinforcement Learning

Computer Science > Artificial Intelligence arXiv:2510.04284 (cs) [Submitted on 5 Oct 2025 (v1), last revised 1 Mar 2026 (this version, v3)] Title:Doctor-R1: Mastering Clinical Inquiry with Experiential Agentic Reinforcement Learning Authors:Yunghwei Lai, Kaiming Liu, Ziyue Wang, Weizhi Ma, Yang Liu View a PDF of the paper titled Doctor-R1: Mastering Clinical Inquiry with Experiential Agentic Reinforcement Learning, by Yunghwei Lai and 4 other authors View PDF HTML (experimental) Abstract:The professionalism of a human doctor in outpatient service depends on two core abilities: the ability to make accurate medical decisions and the medical consultation skill to conduct strategic, empathetic patient inquiry. Existing Large Language Models (LLMs) have achieved remarkable accuracy on medical decision-making benchmarks. However, they often lack the ability to conduct the strategic and empathetic consultation, which is essential for real-world clinical scenarios. To address this gap, we propose Doctor-R1, an AI doctor agent trained to master both of the capabilities by ask high-yield questions and conduct strategic multi-turn inquiry to guide decision-making. Our framework introduces three key components: a multi-agent interactive environment, a two-tiered reward architecture that separately optimizes clinical decision-making and communicative inquiry skills, and an experience repository to ground policy learning in high-quality prior trajectories. We evaluate Doctor-R1 on OpenA...