Beyond Static Evaluation: Building Simulation Environments for Scalable Agentic Reinforcement Learning
Quick Answer
The paper introduces AgenticAI-Supervisor, a novel RL Gym environment designed for scalable agentic reinforcement learning, addressing limitations of static evaluations in multi-step decision-making.
Quick Take
It emphasizes high-fidelity trace generation and multi-dimensional reward shaping while preventing reward hacking through internal state validation, showcased through a Customer Support Agent case study.
Key Points
- AgenticAI-Supervisor decouples environment creation from scalable execution in RL.
- The platform generates high-fidelity traces for improved decision-making analysis.
- Rigorous internal state validation mitigates reward hacking effectively.
- A Customer Support Agent case study demonstrates closed-loop feedback for optimization.
- Future enhancements will include automated stumping and edge-case generation.
Paper Resources
📖 Reader Mode
~2 min readAbstract: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.
| Subjects: | Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2607.05773 [cs.AI] |
| (or arXiv:2607.05773v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2607.05773 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Siddarth Reddy Malreddy [view email]
[v1]
Tue, 7 Jul 2026 02:56:27 UTC (4,428 KB)
— Originally published at arxiv.org
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