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

TL;DR

VLA-JEPA introduces a novel pretraining framework for vision-language-action models that predicts future states in latent space, improving robustness and generalization in video-based tasks by avoiding appearance bias and nuisance motion.

Contribution

It proposes a leakage-free latent state prediction method within a JEPA-style framework, simplifying training and enhancing robustness over prior latent-action approaches.

Findings

Achieves consistent performance gains on LIBERO and real-world manipulation tasks.

Learns dynamics abstractions robust to camera motion and background changes.

Simplifies pretraining with a two-stage process without multi-stage complexity.

Abstract

Pretraining Vision-Language-Action (VLA) policies on internet-scale video is appealing, yet current latent-action objectives often learn the wrong thing: they remain anchored to pixel variation rather than action-relevant state transitions, making them vulnerable to appearance bias, 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 produces latent representations from future frames, while the student pathway sees only the current observation -- future information is used solely as supervision targets, never as input. By predicting in latent space rather than pixel space, VLA-JEPA learns dynamics abstractions that are robust to camera motion and irrelevant background changes. This yields a simple two-stage recipe -- JEPA pretraining followed by action-head fine-tuning -- without the multi-stage complexity of prior latent-action pipelines. Experiments on LIBERO, LIBERO-Plus, SimplerEnv and real-world manipulation tasks show that VLA-JEPA achieves consistent gains in generalization and robustness over existing methods.