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  "text": "Who should design the training environment for an RL agent, the practitioner or the policy itself?\n\nRL pipelines for LLMs usually rely on manually redesigned environments between stages, with practitioners guessing which configuration will best improve the current policy.\n\nThis work proposes an LLM-as-Environment-Engineer framework. The current policy analyzes its own failure trajectories plus context and proposes the next-stage environment configuration, automating a step that has stayed stubbornly manual. They also release MAPF-FrozenLake, a controllable multi-agent testbed whose generator exposes multi-dimensional environment configs.\n\nWhy does it matter?\n\nCurriculum design between RL stages is mostly gut feel today. Letting the policy read its failures and shape the next environment closes a loop that practitioners currently close by hand.\n\nPaper: https://t.co/lZHlqozrQD\n\nLearn to build effective AI agents in our academy: https://t.co/LRnpZN7L4c",
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