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Medicine

Reinforcement Learning for Evidence-Seeking Diagnostic Reasoning with Large Language Models

Shengyi Hua, Kangzhe Hu, Conghui He, Xiaofan Zhang, Shaoting Zhang

Featured July 10, 2026

Original Paper

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Simply

Instead of just guessing, a new AI system teaches language models to act like smart doctors, asking for more tests and refining diagnoses using a realistic simulator that provides plausible results.

In depth
The paper introduces a framework that transforms Large Language Models (LLMs) from passive information processors into active diagnostic assistants. It achieves this by formalizing medical diagnosis as an Iterative Evidence-Seeking Task, leveraging Reinforcement Learning with Verifiable Rewards (RLVR) to guide strategic reasoning, and employing a novel Retrieval-Augmented Generation-based Examination Simulator (RAGES) to provide realistic, knowledge-grounded follow-up evidence.

Key Takeaways

  • 1
    The framework enables LLMs to transition from passive responders to autonomous diagnostic assistants by actively seeking and integrating missing information.
  • 2
    A novel tri-factor reward design within Reinforcement Learning with Verifiable Rewards (RLVR) aligns LLM reasoning with complex clinical logic, incentivizing diagnostic precision and examination consistency.
  • 3
    The Retrieval-Augmented Generation-based Examination Simulator (RAGES) provides a high-fidelity, knowledge-grounded environment for simulating clinical examinations, bridging the gap between static datasets and interactive diagnostic workflows.

Conceptual Flow

HIGH LEVEL
1
Methodology: How AI Learns to Diagnose

The AI starts with patient info, asks for more tests like a doctor, gets simulated results, and then makes a final diagnosis.

Initial Patient Info
Ask for More Tests
Simulated Test Results
2
Results: Smarter, More Accurate Diagnoses

By actively seeking information, the new AI system makes much better and more reliable diagnoses than older methods.

Old Guessing AI
Limited Info
Add New Asking AI
Better Diagnosis