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EnvHarness is a programmable wrapper framework that dynamically adapts static environments to enhance LLM agent training, outperforming existing methods by providing superior optimization signals for reinforcement learning across multiple benchmarks.
This paper proposes the LLM-as-Environment-Engineer framework, where a policy model analyzes failures to automatically redesign the training environment for reinforcement learning, and introduces MAPF-FrozenLake as a controllable testbed. The framework, using Qwen3-4B, outperforms larger models like GPT and Gemini, showing that policy learning improves the model's ability to diagnose weaknesses.
This paper introduces LLM-as-Environment-Engineer, a framework where LLMs design their own training environments for reinforcement learning in multi-agent reasoning tasks, enabling self-improving training that surpasses larger proprietary models.
A technical blog post that explains how to build agent training systems from first principles using a text-to-diagram agent as an example, covering environment definition, teacher trajectory generation, student fine-tuning, and reinforcement learning.