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

[Submitted on 7 Jul 2026]

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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.

Submission history

From: Siddarth Reddy Malreddy [view email]
[v1] Tue, 7 Jul 2026 02:56:27 UTC (4,428 KB)