[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-84003-en":3,"doc-seo-84003-105":30,"detail-sidebar-cat-0-en-105":91},{"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":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":13,"seo_description":14,"update_tm":28,"read_time":29},84003,7971461740909,"Levi","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Beyond Static Evaluation: Building Simulation Environments for Scalable Agentic Reinforcement Learning","Autonomous agent workflows require evaluation beyond static, single-turn benchmarks because success depends on long-horizon planning, error recovery, and multi-step environmental feedback. The paper introduces AgenticAI-Supervisor, an API and UI-driven RL Gym environment that decouples environment creation from scalable execution. Verifiable outcomes produce high-fidelity traces and enable multi-dimensional reward shaping. Rigorous internal state validation reduces reward hacking, and a Customer Support Agent case study demonstrates consistent closed-loop optimization. Future work targets computer/tool use, automated “stumping,” and edge-case generation.","Beyond Static Evaluation: Building Simulation Environments for Scalable Agentic Reinforcement Learning  \nAkshay Arora, Ishan Nigam, Ashutosh Aggarwal, Shefali Bansal, Krishna Singh Sweta Kumari, Nikhil Mittal, Shariq Farhan, Siddarth Malreddy  \nUber AI Solutions  \nSan Francisco, CA, USA  \n[smalreddy@uber.com](smalreddy@uber.com)  \narXiv :2607 .05773v 1 [ cs .AI ] 7 Jul 2026  \nAbstract  \nAs Large Language Models (LLMs) evolve into autonomous agents, traditional static evaluation fails to capture multi-step decisionmaking. We introduce AgenticAI-Supervisor, an API and UI-driven RL Gym environment that decouples environment creation from scalable execution. By moving to verifiable execution outcomes, the platform generates high-fidelity traces and applies multi-dimensional reward shaping. Critically, our framework mitigates reward hacking through rigorous internal state validation and testing. This work provides a first look at our platform’s core capabilities through a Customer Support Agent case study demonstrating a consistent closed-loop feedback for model optimization. Future work will focus on advanced features such as Computer Use, Tool Use, automated\"stumping\", and edge-case generation.  \nACM Reference Format:  \nAkshay Arora, Ishan Nigam, Ashutosh Aggarwal, Shefali Bansal, Krishna Singh, Sweta Kumari, Nikhil Mittal, Shariq Farhan, Siddarth Malreddy. 2026. Beyond Static Evaluation: Building Simulation Environments for Scalable Agentic Reinforcement Learning. In Proceedings of Workshop on RL for Evaluation (RL-Eval ’26) . ACM, New York, NY, USA, 5 pages.  \n1 Introduction  \nLarge Language Models (LLMs) are transitioning from conversational interfaces into autonomous agents capable of reasoning across external tools and complex GUI-based applications [1, 8, 17] . Unlike traditional chatbots, these agents operate in dynamic environments where success depends on long-horizon planning and error recovery [6] . However, as agentic workflows expand, static single-turn benchmarks fail to capture the multi-step decisionmaking and environmental feedback these agents require [1] . Consequently, enterprise-grade models exhibit a severe reliability gap, failing approximately 76% of complex professional tasks due to compounding execution errors [8, 14] . In high-stakes operations such as supply chain auditing or procurement, enterprises cannot rely on systems prone to losing logical consistency or violating implicit constraints over long horizons [9] .  \nPermission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission [and/or a fee. Request permissions from permissions@acm.org](and/or a fee. Request permissions from permissions@acm.org).  \nRL-Eval ’26, San Jose, CA, USA  \n© 2026 Copyright held by the owner/author(s) . Publication rights licensed to ACM.  \nFigure 1: AgenticAI Supervisor enables scalable, verifiable reinforcement learning by combining simulated environments, execution traces, and multi-dimensional rewards.  \nTo establish trust in autonomous systems, the evaluation paradigm must shift from grading textual responses to verifying programmatic actions within large-scale reinforcement learning (RL) lifecycles [16]. While execution traces enable deep-dives into reasoning failure modes [5], the manual authorship of multi-step testcases,“stumping” prompts, and edge scenarios remains an errorprone bottleneck [4]. This creates a scaling deficit, necessitating foundational infrastructure that can automate high-fidelity environment generation and bridge the gap between subjective heu","cbCaiuegGPqHDBsV","https://ap.wps.com/l/cbCaiuegGPqHDBsV","pdf",4064353,4,1,5,"English","en",105,"# Introduction\n## From Static Benchmarks to Interactive Environments\n# Related Work","[{\"question\":\"Why do static evaluation methods fail for agentic reinforcement learning workflows?\",\"answer\":\"Static, single-turn benchmarks cannot reflect multi-step decisionmaking and the environmental feedback agents require. Long-horizon execution also compounds errors, creating reliability gaps in real tasks.\"},{\"question\":\"What is AgenticAI-Supervisor and what problem does it address?\",\"answer\":\"AgenticAI-Supervisor is an API and UI-driven simulation environment built for continuous agent evaluation and optimization. It automates environment creation and execution while producing verifiable outcomes and traces.\"},{\"question\":\"How does the framework reduce reward hacking during training and evaluation?\",\"answer\":\"It uses rigorous internal state mutation testing and strict state validation to ensure trajectories follow business logic rather than exploiting heuristic gaps. The deterministic reward shaping engine penalizes hallucinations and enforces efficiency.\"}]",1784191963,13,{"code":4,"msg":31,"data":32},"ok",{"site_id":25,"language":24,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"beyond-static-evaluation-building-simulation-environments-for-scalable-agentic-reinforcement-learning","",{"@graph":36,"@context":85},[37,53,68],{"@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":20},"https://docshare.wps.com/document/beyond-static-evaluation-building-simulation-environments-for-scalable-agentic-reinforcement-learning/84003/",{"url":52,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":24,"description":14,"dateModified":61,"datePublished":62,"encodingFormat":60,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":41,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-07-26","2026-07-16",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why do static evaluation methods fail for agentic reinforcement learning workflows?","Question",{"text":75,"@type":76},"Static, single-turn benchmarks cannot reflect multi-step decisionmaking and the environmental feedback agents require. Long-horizon execution also compounds errors, creating reliability gaps in real tasks.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What is AgenticAI-Supervisor and what problem does it address?",{"text":80,"@type":76},"AgenticAI-Supervisor is an API and UI-driven simulation environment built for continuous agent evaluation and optimization. It automates environment creation and execution while producing verifiable outcomes and traces.",{"name":82,"@type":73,"acceptedAnswer":83},"How does the framework reduce reward hacking during training and evaluation?",{"text":84,"@type":76},"It uses rigorous internal state mutation testing and strict state validation to ensure trajectories follow business logic rather than exploiting heuristic gaps. The deterministic reward shaping engine penalizes hallucinations and enforces efficiency.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":58,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":25},{"code":4,"msg":5,"data":92},[93,97,101,105,109,114,119,122,127,130,134],{"id":21,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":20,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":22,"doc_module":4,"doc_module_name":46,"category_name":106,"show_sort_weight":107,"slug":108},"Comic",60,"comic",{"id":110,"doc_module":4,"doc_module_name":46,"category_name":111,"show_sort_weight":112,"slug":113},6,"Technology",50,"technology",{"id":115,"doc_module":4,"doc_module_name":46,"category_name":116,"show_sort_weight":117,"slug":118},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":120,"slug":121},30,"research-report",{"id":123,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},9,"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":125,"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":22,"slug":137},19,"General","general"]