Beyond Static Evaluation: Building Simulation Environments for Scalable Agentic Reinforcement Learning
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...
| Authors | Akshay Arora, Ishan Nigam, Ashutosh Aggarwal et al. |
|---|---|
| Primary category | cs.AI |
| Published | 7 July 2026 |
| Last updated at source | 7 July 2026 |
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