Mask-Aware Execution for Efficient JEPA Training
Joint Embedding Predictive Architectures (JEPAs) are becoming a core representation-learning primitive and a building block for latent world models across vision, video, audio, brain dynamics, and time series. Despite (potential of) wide deployment, current JEPA training pipelines are inefficient: each input is executed through multiple mask-specific branches, with redundant target-side work, and memory-bound token routing. These costs grow with the number of masks and limit GPU efficiency. We present M-JEPA, a mask-aware execution architecture that restructures JEPA training without changing the learning objective. M-JEPA separates mask-independent computation from mask-dependent routing...
| Authors | Md Musfiqur Rahman Sanim, Zhihao Shu, Bahram Afsharmanesh et al. |
|---|---|
| Primary category | cs.DC |
| Published | 19 September 2026 |
| Last updated at source | 19 September 2026 |
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