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ResidencyRL: Reinforcement Learning in Simulated Clinical Environments

Research Medical/Healthcare AI

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Merged summary

TL;DR - ResidencyRL is a multi-turn reinforcement learning method that trains clinical LLM agents in simulated patient encounters, mimicking medical residency. It shows that sequential clinical decision-making — not just static benchmark QA — can be learned in simulation and transfers to unseen evaluations.

  • Trains a policy agent against LLM patient/environment simulators capable of complex, adversarial behavior, over trajectories of up to 60 dialogue turns and 8 tool calls.
  • Uses a structured reward spanning diagnostic accuracy, management quality, communication, documentation, and safety.
  • Reported gains: +7.0% diagnostic accuracy under adversarial conditions (88.0% vs. 81.0%), 31% fewer missed red flags, and blinded clinician preference in 87.6% of side-by-side comparisons.
  • Transfers to held-out benchmarks: beats the base model on all six clinical axes of AMIE multi-visit, with directional improvements on AgentClinic and CRAFT-MD; authors note prospective real-world validation is still needed.

Sources (1)

ResidencyRL: Reinforcement Learning in Simulated Clinical Environments

arXiv cs.AI Valentin Liévin, Samuel Schmidgall, Tim Strother, Alex Bijamov, Akshay Goel, Anil Palepu, Chunjong Park, Vahid Balazadeh, Min Woo Sun, Marius Guerard, Justin Chen, Dave Steiner, Vikram Dhillon, Ibrahim Azar, Akhil Mehta, Nicholas Spetsieris, Shilpan Shah, Maen Abdelrahim, Amit Dahiya, Yun Liu, Katherine Chou, Yossi Matias, Avinatan Hassidim, Dale R. Webster, Quoc V. Le, Raia Hadsell, Joelle Barral, Carey Radebaugh, Aleksandra Faust, Shekoofeh Azizi, Mike Schaekermann, Po-Hsuan Cameron Chen, Tao Tu, David Racz, Lin Yang 2026-08-07 arXiv:2608.07418
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Providers: Hugging Face · Upvotes 1 OpenAlex · N/A Publisher · N/A Semantic Scholar · N/A X · N/A Fetched 2026-09-07 14:27:18.171573 UTC

TL;DR - ResidencyRL is a multi-turn reinforcement learning method that trains clinical LLM agents in simulated patient encounters, mimicking medical residency. It shows that sequential clinical decision-making — not just static benchmark QA — can be learned in simulation and transfers to unseen evaluations.

  • Trains a policy agent against LLM patient/environment simulators capable of complex, adversarial behavior, over trajectories of up to 60 dialogue turns and 8 tool calls.
  • Uses a structured reward spanning diagnostic accuracy, management quality, communication, documentation, and safety.
  • Reported gains: +7.0% diagnostic accuracy under adversarial conditions (88.0% vs. 81.0%), 31% fewer missed red flags, and blinded clinician preference in 87.6% of side-by-side comparisons.
  • Transfers to held-out benchmarks: beats the base model on all six clinical axes of AMIE multi-visit, with directional improvements on AgentClinic and CRAFT-MD; authors note prospective real-world validation is still needed.
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