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

Researchers introduced AgenticAI-Supervisor, an API and UI-driven RL Gym environment that decouples environment creation from scalable execution for training LLM-based autonomous agents. The platform generates high-fidelity traces with multi-dimensional reward shaping and mitigates reward hacking through internal state validation. A Customer Support Agent case study demonstrated consistent closed-loop feedback for model optimization.

arXiv:2607.05773v1 Announce Type: new 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.