Jarvis1111/DoctorAgent-RL-SFT-1k-Thinking
Jarvis1111/DoctorAgent-RL-SFT-1k-Thinking is a 7.6 billion parameter model developed by JarvisUSTC, built upon the Qwen2.5-7B-Instruct base. This model employs a novel reinforcement learning (RL)-based multi-agent collaborative framework to optimize multi-turn clinical dialogues. It excels at dynamic diagnostic reasoning and questioning strategies, aiming to improve diagnostic accuracy and resource allocation in medical consultations. The model is specifically fine-tuned for medical consultation scenarios, addressing challenges like vague diagnoses and static dialogue models.
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DoctorAgent-RL: Multi-Agent Clinical Dialogue System
DoctorAgent-RL is a 7.6 billion parameter model developed by JarvisUSTC, based on the Qwen2.5-7B-Instruct architecture. It introduces a novel reinforcement learning (RL)-based multi-agent collaborative framework designed to model medical consultations as dynamic decision-making processes under uncertainty. This system features a Doctor Agent that continuously optimizes its questioning strategy through multi-turn interactions with a Patient Agent, guided by comprehensive rewards from a Consultation Evaluator.
Key Capabilities
- Dynamic Strategy Optimization: Utilizes reinforcement learning for continuous policy updates, enabling adaptive dialogue behavior and information gathering aligned with clinical reasoning.
- Multi-Agent Collaboration: Employs distinct Doctor and Patient agents, each with specific roles, to simulate realistic medical consultations.
- Comprehensive Reward Design: Guides optimal strategies using multi-dimensional evaluation metrics for consultation quality.
- Enhanced Diagnostic Performance: Experiments demonstrate superior multi-turn reasoning and final diagnostic accuracy compared to existing models.
- MTMedDialog Dataset: Introduces the first English multi-turn medical consultation dataset, facilitating patient interaction simulations.
Good For
- Improving Diagnostic Accuracy: Reduces misdiagnosis risks by enabling more thorough and adaptive information gathering.
- Optimizing Medical Resource Allocation: Streamlines consultation processes through efficient and clinically aligned dialogue strategies.
- Simulating Clinical Scenarios: Provides a robust framework for training and evaluating AI in complex medical dialogue environments.