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.

要旨より。当社による要約は作成中です。