Real-World Doctor Agent with Proactive Consultation through Multi-Agent Reinforcement Learning

Understand

Large language models (LLMs) struggle in real-world clinical consultations.

• Single-turn consultation systems require patients to describe all symptoms at once, which often leads to unclear complaints and vague diagnoses.
• Traditional dialogue models, constrained by static supervised learning, are limited to superficially imitating existing dialogue patterns and lack the ability to actively construct understanding in dynamic interactions, thus failing to achieve genuine clinical reasoning.To address these challenges, we propose DoctorAgent-RL, a reinforcement learning (RL)-based multi-agent collaborative framework, and train a doctor agent on Qwen2.5-7B-Instruct using this framework.
• Within this framework, a medical consultation is modeled as a dynamic decision-making process under uncertainty.