Anisotropic Representations Improve Planning in JEPA World Models
Latent world models learn action-conditioned dynamics in representation space and often score candidate actions by Euclidean distance to a goal representation. Joint training typically regularizes the representation to prevent collapse, but the resulting representation geometry also determines how terminal errors are weighted during planning. We show that accurate prediction an
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Latent world models learn action-conditioned dynamics in representation space and often score candidate actions by Euclidean distance to a goal representation. Joint training typically regularizes the representation to prevent collapse, but the resulting representation geometry also determines how terminal errors are weighted during planning. We show that accurate prediction an
This research addresses a critical challenge in latent world models for planning by improving how latent representations align with task costs. For ML engineers, understanding this can lead to more effective agent design and better performance in complex decision-making tasks.