World Feedback for Clinical Agents: Diagnosing RL in FHIR Environments
Quick Answer
The study introduces MedAgentBench-v3, enhancing RL in clinical tasks with a reduced ceiling of 8.9%.
Quick Take
It identifies critical barriers in RL learnability, showing pure RL achieves only 18.2% pass@1 compared to 34.1% for rule-based methods, emphasizing the need for structured feedback and code injection.
Key Points
- MedAgentBench-v3 features 508 tasks with an 8.9% performance ceiling.
- Pure RL achieved 18.2% pass@1, while rule-based SFT reached 34.1%.
- Identified barriers include capability ceiling and format-knowledge issues.
- Training Qwen3-8B revealed significant structural limitations in RL.
- The study advocates for SFT to inject codes and RL for learning conditionals.
Paper Resources
📖 Reader Mode
~2 min readAbstract: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 performance, zero gradient) and a \emph{format-knowledge barrier} (3/20 types require exact clinical codes undiscoverable by exploration). Pure RL reaches 18.2\% pass@1 vs.\ 34.1\% for rule-based SFT; the 15.9~pp gap is attributable entirely to these barriers. A decision/format-knowledge/lookup taxonomy predicts RL learnability and prescribes the fix: SFT to inject codes, RL to learn conditionals.
| Subjects: | Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2607.01470 [cs.AI] |
| (or arXiv:2607.01470v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2607.01470 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Harshit Rajgarhia [view email]
[v1]
Wed, 1 Jul 2026 21:02:54 UTC (130 KB)
— Originally published at arxiv.org
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