Building a First World Model › Summary

• A world model predicts environmental consequences of actions from an internal latent state, enabling search over futures without touching the real environment.
• The minimal architecture has four parts: an encoder, a transition model, a decoder, and a reward head, trained jointly by observation reconstruction and reward prediction.
• Filtering folds fresh observations into the latent state and corrects errors; imagination rolls the transition model forward on actions alone. Planning needs both.
• Teacher-forced accuracy and recursive rollout accuracy are different properties. Only the second matters for planning, and it is strictly harder, because errors compound and the model visits its own outputs.
• Latent memory is decisive under partial observability. In our experiment, removing the recurrent update left the model unable to represent velocity, visibly degrading both its predictions and its control performance.
• Receding-horizon MPC contains model imperfection by replanning every step from freshly filtered beliefs, trading computation for robustness.
• The remaining gaps, compounding error, stochasticity, model exploitation, and uncertain beliefs, are not defects of this particular implementation but structural research problems that organize the rest of this handbook.

With a concrete system in hand, we can now ask the question this chapter deliberately postponed: what, precisely, makes an object a world model, and what should we demand of one? That is the business of the next chapter.