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
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 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
Source Excerpt
arXiv:2606. 17682v1 Announce Type: new Abstract: Reinforcement learning pipelines for (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. …
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