HealthCraft: A Reinforcement Learning Safety Environment for Emergency Medicine

[2605.21496] HealthCraft: A Reinforcement Learning Safety Environment for Emergency Medicine

[Submitted on 18 Apr 2026]

Title:HealthCraft: A Reinforcement Learning Safety Environment for Emergency Medicine

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Abstract:Frontier language models are being deployed into clinical workflows faster than the infrastructure to evaluate them safely. Static medical-QA benchmarks miss the failure modes that matter in emergency medicine: trajectory-level safety collapse, tool misuse, and capitulation under sustained clinical pressure. We present HealthCraft, the first public reinforcement-learning environment that rewards trajectory-level safety under realistic emergency-medicine conditions, adapted from Corecraft. It is built on a FHIR R4 world state with 14 entity types and 3,987 seed entities, exposes 24 MCP tools, and defines a dual-layer rubric that zeroes reward whenever any safety-critical criterion is violated. We release 195 tasks across six categories, graded against 2,255 binary criteria (515 safety-critical); a post-hoc 10-task negative-class slate extends this to 205 tasks and 2,337 criteria. V8 results on two frontier models show Claude Opus 4.6 at Pass@1 24.8% [21.5-28.4] and GPT-5.4 at 12.6% [10.2-15.6], with safety-failure rates of 27.5% and 34.0%. On multi-step workflows - the closest proxy to real emergency care - performance collapses to near zero (Claude 1.0%, GPT-5.4 0.0%) despite partial competence on individual steps. Six infrastructure bugs fixed between pilots v2 and v8 re-ordered which model "looks stronger," evidence that infrastructure fidelity is part of the measurement. A deterministic LLM-judge overlay bounds evaluator noise, and a 60-run negative-class smoke pilot shows the reward signal is not drop-in training-safe: restraint criteria pass at 0.929 prevalence, a gameability an eval harness can tolerate but a training reward cannot. We scaffold coupling to a Megatron+SGLang+GRPO loop per Corecraft Section 5.2 and leave training-reward ablations as future work. Environment, tasks, rubrics, and harness are released under Apache 2.0.

Comments: 16 pages, 5 figures, 6 tables. Code, task suite, and Docker bundle: this https URL

Subjects:

Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computation and Language (cs.CL)

MSC classes: 68T05, 92C50

ACM classes: I.2.6; I.2.7; J.3

Cite as: arXiv:2605.21496 [cs.LG]

(or arXiv:2605.21496v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2605.21496

arXiv-issued DOI via DataCite

Submission history

From: Brandon Dent M.D. [view email] [v1] Sat, 18 Apr 2026 03:40:24 UTC (55 KB)

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