mlsanityData to 7 Oct 2026

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

arXiv 2607.05773 ↗First posted 7 Jul 2026cs.AIcited by 0

On the map: LLM agents and tool use → Reinforcement learning for LLM agents

Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Reinforcement learning for LLM agents, one of 16 directions in LLM agents and tool use. Builds on 3 of its 12 references that are in the map. First posted on arXiv 7 Jul 2026.

Builds on

3 of 12 references.

References · 3

  1. 2024TheAgentCompany: Benchmarking LLM Agents on Consequential Real World Taskscited by 306
  2. 2025Survey on Evaluation of LLM-based Agentscited by 190
  3. 2026ReliabilityBench: Evaluating LLM Agent Reliability Under Production-Like Stress Conditionscited by 31

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  1. 2026AgentV-RL: Scaling Reward Modeling with Agentic Verifier
  2. 2026Scaling Agentic Capabilities, Not Context: Efficient Reinforcement Finetuning for Large Toolspaces
  3. 2025Agent-R1: A Unified and Modular Framework for Agentic Reinforcement Learning
  4. 2026RLAR: An Agentic Reward System for Multi-task Reinforcement Learning on Large Language Models
  5. 2026Beyond Static Snapshots: A Grounded Evaluation Framework for Language Models at the Agentic Frontier
  6. 2025A Practitioner's Guide to Multi-turn Agentic Reinforcement Learning
  7. 2025AgentFly: Extensible and Scalable Reinforcement Learning for LM Agents
  8. 2024Online Intrinsic Rewards for Decision Making Agents from Large Language Model Feedback