Beyond Static Evaluation: Building Simulation Environments for Scalable Agentic Reinforcement Learning
For researchers developing autonomous LLM agents, this work provides a scalable platform for training and evaluating multi-step decision-making, though it is an incremental step with no quantitative results.
The paper introduces AgenticAI-Supervisor, a simulation environment for reinforcement learning of LLM-based agents, addressing the limitations of static evaluation for multi-step decision-making. It demonstrates consistent closed-loop feedback for model optimization in a customer support agent case study.
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.