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ResidencyRL: Reinforcement Learning in Simulated Clinical Environments

Valentin Liévin, Samuel Schmidgall, Tim Strother, Alex Bijamov, Akshay Goel, Anil Palepu · Aug 7, 2026

Citations: 0

Match reason: Matched by broad semantic/index fallback.

Score: 38% High protocol signal Freshness: Warm Status: Ready
Expert Verification Automatic MetricsSimulation Env Long Horizon Medicine
  • We present ResidencyRL, a reinforcement learning (RL) method for training clinical artificial intelligence (AI) agents through simulated multi-turn clinical encounters (up to 60 dialogue turns and 8 tool calls per trajectory).
  • ResidencyRL pairs the policy agent with LLM simulators capable of complex, adversarial behaviors, training against a structured reward aligned to diagnostic accuracy, management quality, communication, documentation, and safety.
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