True Agents Model the World
World modeling during RL trains models to predict their environment in response to their own actions, improving in-domain generalization and efficiency in early ECHO experiments.
True Agents Model the World Pretraining creates simulators , LLMs that can model their environment with high precision. RL only improves the model’s own generations. But true agents should do both at the same time: we don’t want a pure simulator, but neither do we want the LLM to only act, blind to how it will affect its environment. Instead, we want it to learn to predict its environment in response to its own actions, allowing it to more intelligently plan and respond to unexpected world dynamics. Synthetic data during pre- and midtraining achieves similar objectives, but only world modeling
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