[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-81817-en":3,"doc-seo-81817-105":30,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":11,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},81817,4398048950312,"Violet","https://ap-avatar.wpscdn.com/avatar/400002538284de19e3c?_k=1778320343897328908",8,"Research & Report","World Feedback for Clinical Agents Diagnosing RL in FHIR Environments","Clinical protocol-execution tasks—retrieving a lab value, applying a threshold, and generating a correctly structured FHIR order—are natural reinforcement learning (RL) candidates when “world feedback” comes from an auditable verifier encoded by clinical SMEs. The document audits MedAgentBench v1/v2, identifies a 41.7% silent-finish ceiling that makes inaction dominant, and introduces MedAgentBench-v3 (MAB-v3, 508 tasks). Qwen3-8B RL reveals capability and format-knowledge barriers, yielding 18.2% pass@1 vs 34.1% for rule-based SFT; SFT+RL is prescribed.","World Feedback for Clinical Agents: Diagnosing RL in FHIR Environments  \nAnanya Mantravadi 1 Harshit Rajgarhia 1 Prasanna Desikan 1 Abhishek Mukherji 1  \narXiv :2607 .0 1470v 1 [ cs .AI] 1 Jul 2026  \nAbstract  \nClinical protocol-execution tasks—checking alab value, applying a threshold, placing a correctly structured FHIR order—are natural candidates for RL from world feedback: once clin  \nical 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 MedAgentBench-v3 (MAB-v3) (508 tasks, 8.9% ceiling) . Training Qwen3-8B exposes two structural barriers: a capability ceiling (10/20 task types have 0% base performance, zero gradient) and a 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.  \n1. Introduction  \nA large class of clinical tasks involves protocol execution: given a known decision rule, the agent retrieves a lab value, applies the threshold, and if triggered places a correctly structured FHIR order. These are administrative workflow tasks—not replacement of physician judgment, but execution of standing orders (Jiang et al., 2025 ; Lee et al., 2025 ; Bedi et al., 2026) . MedAgentBench v1 and v2, defining the 20 task types studied here, were constructed with clinical teams who validated each workflow against real EHR practice (Jiang et al., 2025 ; Chen et al., 2025) .  \n1 Centific Global Solutions, Inc.. Correspondence to: Ananya Mantravadi \u003C[ananya.mantravadi@centific.com](ananya.mantravadi@centific.com) >.  \nProceedings of the 43 rd International Conference on Machine Learning, Seoul, South Korea. PMLR 306, 2026 . Copyright 2026 by the author(s) .  \nWhy RL from world feedback: Protocol correctness is verifiable: once a clinical SME encodes the decision logic into a verifier, that verifier grades every subsequent rollout automatically. This is qualitatively different from RLHF (Christiano et al., 2017 ; Ouyang et al., 2022): SME effort is front-loaded into environment design rather than spent labeling individual episodes. The alternative—supervised SFT demos—requires manually coding every clinical rule, is biased toward action-branch instances, and must be regenerated when protocols change. RL from world feedback avoids this: the agent explores the environment and receives feedback from the verifier, with only a single update needed when a protocol changes.  \nWhat our experiments reveal: Applying RL here is non-trivial. Two structural barriers limit a naive approach. First, the feedback channel must be clean: MedAgentBench v1/v2 had a 41.7% silent-finish ceiling (41.7% of tasks pass with no tool use), making inaction the RL dominant strategy. GRPO (Shao et al., 2024) on uncorrected MAB-v2 converged to 0% action-branch pass. We construct MedAgentBench-v3 (MAB-v3) (508 tasks, 8.9% ceiling) to fix this. Second, even on a clean benchmark, 3 of 20 task types require exact clinical codes (SNOMED, NDC) undiscoverable by exploration—flat reward landscape—and 10 types have zero base capability, yielding zero gradient. These two barriers explain the 15.9 pp gap: SFT (34.1%) injects codes and format; pure RL (18.2%) cannot. The approaches are complementary, and SFT+RL is the prescription our results motivate.  \nContributions:  \n• MAB-v3 + environment (Section 3, 4.1): Corrected 508-task benchmark (silent-finish ceiling 41.7%→8.9%) with a self-contained world feedback environment: offline FHIR server, auditable rule-based verifier, and deliberate reward shaping for con","cbCaio9zGGZExTaG","https://ap.wps.com/l/cbCaio9zGGZExTaG","pdf",339141,7,1,"English","en",105,"# Introduction\n# Related Work\n# MedAgentBench-v3: Restoring the World Feedback Signal","[{\"question\":\"Why does the paper argue that RL from world feedback fits clinical protocol execution?\",\"answer\":\"Protocol correctness is verifiable once clinical SMEs encode decision logic into a verifier, which grades every rollout automatically. This front-loads SME effort into environment design rather than per-episode labeling.\"},{\"question\":\"What are the main barriers that prevent naive RL from working well on MedAgentBench?\",\"answer\":\"The paper reports a feedback-channel problem via a silent-finish ceiling (inaction becomes dominant) and a task-structure problem where some types require exact clinical codes and others have zero base capability, producing flat rewards or zero gradient.\"},{\"question\":\"How does MedAgentBench-v3 change the benchmark and what improvement is reported?\",\"answer\":\"MAB-v3 corrects the 508-task benchmark and restores learnable world-feedback signal by reducing the silent-finish ceiling from 41.7% to 8.9%, enabling more effective training signal compared with v1/v2.\"}]","World Feedback for Clinical Agents Diagnosing RL in FHIR Environments | PDF",1784176340,20,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":28},"world-feedback-for-clinical-agents-diagnosing-rl-in-fhir-environments","",{"@graph":36,"@context":86},[37,54,69],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/world-feedback-for-clinical-agents-diagnosing-rl-in-fhir-environments/81817/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-03","2026-07-16",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"Why does the paper argue that RL from world feedback fits clinical protocol execution?","Question",{"text":76,"@type":77},"Protocol correctness is verifiable once clinical SMEs encode decision logic into a verifier, which grades every rollout automatically. This front-loads SME effort into environment design rather than per-episode labeling.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"What are the main barriers that prevent naive RL from working well on MedAgentBench?",{"text":81,"@type":77},"The paper reports a feedback-channel problem via a silent-finish ceiling (inaction becomes dominant) and a task-structure problem where some types require exact clinical codes and others have zero base capability, producing flat rewards or zero gradient.",{"name":83,"@type":74,"acceptedAnswer":84},"How does MedAgentBench-v3 change the benchmark and what improvement is reported?",{"text":85,"@type":77},"MAB-v3 corrects the 508-task benchmark and restores learnable world-feedback signal by reducing the silent-finish ceiling from 41.7% to 8.9%, enabling more effective training signal compared with v1/v2.","https://schema.org",{"og:url":52,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":93},[94,98,102,106,111,116,120,123,127,130,134],{"id":21,"doc_module":4,"doc_module_name":46,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":46,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":112,"doc_module":4,"doc_module_name":46,"category_name":113,"show_sort_weight":114,"slug":115},6,"Technology",50,"technology",{"id":20,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":29,"slug":126},9,"Religion & Spirituality","religion-spirituality",{"id":29,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":29,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":107,"slug":137},19,"General","general"]