From Trainee to Trainer
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Who should design the training environment for an RL agent, the practitioner or the policy itself? RL pipelines for LLMs usually rely on manually redesigned environments between stages, with practitioners guessing which configuration will best improve the current policy. This paper hands that job to the model, proposing an LLM-as-Environment-Engineer framework where the policy diagnoses its own weaknesses and proposes the next environment to train on.
The policy designs its own curriculum: Instead of a human reshaping the environment between stages, the current policy analyzes failure trajectories together with contextual information and proposes modifications to the next-stage training environment configuration.
Failure-driven environment edits: Because the proposals are grounded in the policy's actual failure modes, the curriculum targets the specific gaps holding the model back rather than generic difficulty bumps.
The trainee becomes the trainer: A key finding is that the current RL checkpoint serves as a better environment engineer than the original base model, suggesting that learning to act also improves the model's ability to diagnose what it still cannot do.
Why it matters: Manual between-stage environment design is one of the least scalable parts of RL for LLMs, and letting the policy steer its own curriculum closes a slow human-in-the-loop step that has bottlenecked agentic RL.
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