VLA-JEPA: Enhancing Vision-Language-Action Model with Latent World Model

Pretraining Vision-Language-Action (VLA) policies on internet-scale video is appealing, yet current latent-action objectives often learnthe wrong thing: they remain anchored to pixel variation rather thanaction-relevant state transitions, making them vulnerable to appearancebias, nuisance motion, and information leakage. We introduce VLA-JEPA,a JEPA-style pretraining framework that sidesteps these pitfalls by design.The key idea is leakage-free state prediction: a target encoder produceslatent representations from future frames, while the student pathwaysees only the current observation—future information is used solely assupervision targets, never as input. By predicting in latent space ratherthan pixel space, VLA-JEPA learns dynamics abstractions that are ro-bust to camera motion and irrelevant background changes. This yieldsa simple two-stage recipe—JEPA pretraining followed by action-headfine-tuning—without the multi-stage complexity of prior latent-actionpipelines. Experiments on LIBERO, LIBERO-Plus, SimplerEnv and real-world manipulation tasks show that VLA-JEPA achieves consistent gainsin generalization and robustness over existing methods. Code is availableat https://github.com/ginwind/VLA-JEPA.