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Current world models fail to generalize to novel task combinations, even when individual components are familiar, suggesting a fundamental limitation in their compositional understanding of environment dynamics.
Transition effects can be disentangled into reusable primitives, leading to superior policy learning even in complex, ambiguous environments.