AGENTIC-SYSTEMSJuly 8, 20261 min read

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. This paper introduces AgenticAI-Supervisor, an RL Gym environment designed to enable scalable, multi-step decision-making evaluation and mitigate reward hacking.

Computer Science > Artificial Intelligence

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

View PDF HTML (experimental)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.

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Source: arXiv cs.AI Recent

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