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...
Expected Reasoning-Step Return is introduced, which treats semantic reasoning steps as macro-actions and uses Monte Carlo student-policy rollouts to estimate the expected final task reward of student-generated and teacher-proposed actions in a common return space for step-level comparison.
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.
Calibrated On-Policy Distillation is introduced, which estimates the teacher's self-deviation region through positive and negative privileged interventions and calibrates the original teacher--student discrepancy by retaining only the component that lies beyond this region.
Qiang-Qiang He, Jin Li, Ming-Cai Chen· 2 citations· ⚡1
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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