Heejeong Nam, Divyat Mahajan, Quentin Le Lidec, Randall Balestriero, David Klindt · 2026 · arXiv
DOI: 10.48550/arXiv.2610.26844 · open-access PDF
The goal of a world model is to capture the dynamics of an environment. This has the great appeal that it allows agents to plan and simulate a course of actions with the help of a world model. For these uses to be reliable, however, the learned dynamics must behave well beyond the specific state-action combinations observed in expert rollouts. Despite this, the relationship between data coverage, dynamics generalization, and downstream planning performance remains underexplored. We study this relationship through the lens of compositional generalization: can a world model recombine familiar st
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