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This paper examines the use of reinforcement learning from world feedback for clinical protocol-execution tasks in FHIR environments, identifies structural barriers like high silent-finish ceilings and zero-gradient tasks, and introduces MedAgentBench-v3 with a lower ceiling. It shows that pure RL underperforms rule-based SFT due to these barriers, and proposes a combined SFT+RL approach.
This paper explores transfer learning for mapping FHIR questionnaire items to LOINC codes using retrieval methods, comparing six approaches on a small evaluation set.
This paper presents a reinforcement learning post-training pipeline for tool-calling LLM agents operating on FHIR healthcare data, achieving a 77% answer correctness on FHIR-AgentBench using a smaller Qwen3-8B model compared to 50% with o4-mini.