World Feedback for Clinical Agents: Diagnosing RL in FHIR Environments
Clinical protocol-execution tasks -- checking a lab value, applying a threshold, placing a correctly structured FHIR order -- are natural candidates for RL from world feedback: once clinical SMEs encode decision logic into a verifier, that verifier grades unlimited rollouts without per-episode annotation. But applying RL requires a sound feedback channel and sufficient base capability. We audit MedAgentBench v1/v2, find a 41.7\% silent-finish ceiling that makes inaction the RL dominant strategy, and construct \textbf{MedAgentBench-v3 (MAB-v3)} (508 tasks, 8.9\% ceiling). Training Qwen3-8B exposes two structural barriers: a \emph{capability ceiling} (10/20 task types have 0\% base performa...
| Authors | Ananya Mantravadi, Harshit Rajgarhia, Prasanna Desikan et al. |
|---|---|
| Primary category | cs.AI |
| Published | 1 July 2026 |
| Last updated at source | 1 July 2026 |
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