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

2026-07-06

AI Summary

Existing static evaluation methods struggle to effectively assess the agentic capabilities of large language models in multi-step decision-making tasks. To address this limitation, this work proposes AgenticAI-Supervisor, the first reinforcement learning simulation framework for agentic AI that supports closed-loop feedback. By decoupling environment construction from scalable execution, the framework integrates API- and UI-driven Gym environments, generates high-fidelity execution trajectories, employs multi-dimensional reward shaping, and incorporates internal state validation mechanisms to mitigate reward hacking. Demonstrated in a customer service agent case study, the framework enables stable closed-loop feedback and significantly enhances model optimization outcomes.

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.

Problem

• agentic reinforcement learning
• static evaluation
• simulation environments
• multi-step decision-making
• reward hacking

Innovation

• Agentic Reinforcement Learning
• Simulation Environment
• Reward Shaping
• Reward Hacking Mitigation
• Scalable Execution