Diffusion transformers (DiTs) have become the dominant architecture for high-fidelity image synthesis, yet they suffer from slow training convergence and high computational cost. Two recent lines of work address this from complementary angles: Representation Alignment (REPA) accelerates DiT training by aligning interme...
AWM-VLA is presented, a unified framework that embeds aligned world modeling directly inside a diffusion-transformer policy and is compatible with any diffusion or flow-matching policy, making aligned world modeling an inexpensive, broadly applicable component of generalist manipulation.
GT-Space constructs a common feature space from ground-truth labels, providing a unified reference for feature alignment, and designs a fusion network trained with contrastive losses across diverse modality combinations.
UniJEPA is presented, a unified JEPA that jointly learns photometric prediction (image-level transformations) and temporal prediction (video-level next-state dynamics) in one shared latent space and shows that the same latent space supports controllable abstraction.
Lan-Ji An, Da-Wei Liu, Jin Li et al.· 1 citation
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