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.
Cite
@article{arxiv.2607.05773,
title = {Beyond Static Evaluation: Building Simulation Environments for Scalable Agentic Reinforcement Learning},
author = {Akshay Arora and Ishan Nigam and Ashutosh Aggarwal and Shefali Bansal and Krishna Singh and Sweta Kumari and Nikhil Mittal and Shariq Farhan and Siddarth Malreddy},
journal= {arXiv preprint arXiv:2607.05773},
year = {2026}
}