La-JEPA: Compact Robot Policies with Latent World Modeling

World-action models commonly adapt pretrained video generators, and vision-language-action models build on pretrained vision-language backbones; both tie policy architecture and inference cost to large transformers designed for generation rather than control. While the knowledge accumulated through large-scale pretraining is certainly useful, it remains unclear how much generation-oriented architectural choices affect control performance, and whether the trade-off required to access this pretraining is worth it. In this work, we introduce **Latent Action JEPA (La-JEPA)**, a compact world-action model whose policy transformer is trained from scratch in the feature space of a pretrained V-JEPA 2.1 encoder, so that its architecture and capacity can be chosen for control. Following Fast-WAM, La-JEPA learns to predict future features jointly with actions during training and generates only actions at inference. **Among policies without embodied pretraining, La-JEPA achieves state-of-the-art clean and randomized success on RoboTwin 2.0 and is equivalent to the state of the art on LIBERO-Plus, while using far fewer parameters than the strongest competing policies.** It runs substantially faster than Fast-WAM at comparable performance and outperforms the closest JEPA-based methods, LeapBot-WA and JEPA-WAM, on LIBERO-Plus and RoboTwin 2.0 without their pretrained policy backbones. On four real-robot tasks, including contact-heavy pushing and long-horizon multistep manipulation, its average success is comparable to ${\pi }_{0.5}$, outperforming non-embodied-pretrained baselines. Through a design study of the choices behind La-JEPA, we show that JEPA features outperform video VAE latents, most clearly out of distribution, and that training-time future prediction choices significantly impact the policy even when future prediction is skipped at inference. These results establish a compact, efficient foundation for JEPA-based world-action modeling: without a generative pretrained policy backbone, La-JEPA is comparable to or better than far larger policies built on video and language generation models, suggesting that robot foundation models may benefit from architectures designed for control rather than inherited from generation.