{"ID":6023346,"CreatedAt":"2026-07-08T01:00:23.257252134Z","UpdatedAt":"2026-07-10T01:44:12.350457273Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2607.05773","arxiv_id":"2607.05773","title":"Beyond Static Evaluation: Building Simulation Environments for Scalable Agentic Reinforcement Learning","abstract":"As Large Language Models (LLMs) evolve into autonomous agents, traditional static evaluation fails to capture multi-step decision-making. 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.","short_abstract":"As Large Language Models (LLMs) evolve into autonomous agents, traditional static evaluation fails to capture multi-step decision-making. 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, th...","url_abs":"https://arxiv.org/abs/2607.05773","url_pdf":"https://arxiv.org/pdf/2607.05773v1","authors":"[\"Akshay Arora\",\"Ishan Nigam\",\"Ashutosh Aggarwal\",\"Shefali Bansal\",\"Krishna Singh\",\"Sweta Kumari\",\"Nikhil Mittal\",\"Shariq Farhan\",\"Siddarth Malreddy\"]","published":"2026-07-07T02:56:27Z","proceeding":"cs.AI","tasks":"[\"cs.AI\"]","methods":"[\"Reinforcement Learning\",\"Large Language Model\",\"Language Model\"]","has_code":false}