Replace VL-JEPA’s InfoNCE with a sample-independent anti-collapse regularizer

Investigate replacing the bidirectional InfoNCE loss in VL-JEPA with a sample-independent anti-collapse regularizer and determine whether such a formulation can prevent collapse and enable effective training without batch-level negatives.

Background

VL-JEPA frames vision–language learning as predictive alignment between image-predicted and text target embeddings but uses a bidirectional InfoNCE loss, inheriting batch-negative sampling and batch-size dependence. The paper notes that VL-JEPA explicitly mentions the possibility of substituting InfoNCE with a sample-independent anti-collapse regularizer, which would eliminate the reliance on negatives, but that prior work left this substitution to future work.