Beyond Static Evaluation: Building Simulation Environments for Scalable Agentic Reinforcement Learning

• URL: http://arxiv.org/abs/2607.05773v1
• Date: Tue, 07 Jul 2026 02:56:27 GMT
• Title: Beyond Static Evaluation: Building Simulation Environments for Scalable Agentic Reinforcement Learning
• Abstract summary: AgenticAI-Supervisor is an API and UI-driven RL Gym environment that decouples environment creation from scalable execution.<n>By moving to verifiable execution outcomes, the platform generates high-fidelity traces and applies multi-dimensional reward shaping.
• Score: 2.4997990870443774
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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.

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