World models for RL is an underrated research direction, argues OpenAI dev
willdepue · x · 2026-09-22
Developer willdepue argues that world models for reinforcement learning are an underrated research direction, potentially allowing training on a reasonable fraction of production data without access to the real environment. His proposed recipe: take production/user data with negative feedback, build a synthetic environment via a "world model" simulator that mocks tool-call results, and train in that domain — the same trick robotics uses when the environment is hard to access.
Related event: World Models for RL Training an Underrated Direction(3 posts)→
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