From Trainee to Trainer: LLM-Designed Training Environment for RL with Multi-Agent Reasoning
Quick Answer
This paper shows that The LLM-as-Environment-Engineer framework automates reinforcement learning environment redesign, achieving superior performance with Qwen3-4B over larger models like GPT and Gemini.
Quick Take
The LLM-as-Environment-Engineer framework automates reinforcement learning environment redesign, achieving superior performance with Qwen3-4B over larger models like GPT and Gemini. It utilizes failure trajectories and contextual information to enhance training configurations, demonstrating that current RL checkpoints can better diagnose weaknesses than original models.
Key Points
- Introduces MAPF-FrozenLake, a testbed for multi-dimensional environment configurations.
- Qwen3-4B outperforms larger proprietary LLMs in benchmark tests.
- Environment updates rely on failure evidence and successful configurations.
- Current RL checkpoints are more effective than original models for environment engineering.
- Framework automates the redesign process, reducing manual inference needs.
Paper Resources
Article Content
From source RSS / original summaryarXiv:2606. 17682v1 Announce Type: new Abstract: Reinforcement learning pipelines for Large Language Model (LLM) training often rely on manually redesigned environments between stages, requiring practitioners to heuristically infer which configuration will best improve the current policy.
To automate this process, we propose the LLM-as-Environment-Engineer framework in which the current policy model analyzes failure trajectories together with contextual information and proposes modifications to the next-stage training environment configuration. We also introduce MAPF-FrozenLake, a controllable testbed whose generator exposes multi-dimensional environment configurations, making it suitable for studying and benchmarking environment redesign.
On this testbed, we condition the environment engineer on structured summaries of policy behavior, failure cases, and environment statistics, from which it produces the configuration for the next training stage. With Qwen3-4B as the backbone, our framework achieves the strongest aggregate performance on our benchmarks, outperforming larger proprietary LLMs (e. g. , GPT, Gemini) and fixed-environment training baselines.
We further analyze which forms of context are most effective, finding that successful environment updates rely on failure evidence and preserve configurations that already work. Interestingly, the current RL checkpoint serves as a better environment engineer than the original base model, suggesting that policy learning improves the model's ability to diagnose its remaining weaknesses.
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