# JEPA-VLA: Video Predictive Embedding is Needed for VLA Models arXiv: https://arxiv.org/abs/2602.11832 · HTML: https://mlsanity.com/paper/2602.11832/ ## Key facts - Cited by 24 papers in the map (data through 14 Oct 2026). - Placed in Embodied and 3D vision-language models, one of 16 directions in Multimodal LLMs / vision-language models. - Builds on 39 of its 43 references that are in the map. - First posted on arXiv 12 Feb 2026. ## Placed in - Multimodal LLMs / vision-language models / [Embodied and 3D vision-language models](https://mlsanity.com/mllm/embodied-and-3d-vision-language-models/) - Vision-language-action models / [World model-based VLA](https://mlsanity.com/vla/world-model-based-vla/) ## Builds on (most cited first, 30 of 39) - [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021) - [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020) - [DINOv2: Learning Robust Visual Features without Supervision](https://mlsanity.com/paper/2304.07193/) (2023) - [Emerging Properties in Self-Supervised Vision Transformers](https://mlsanity.com/paper/2104.14294/) (2021) - [Flamingo: a Visual Language Model for Few-Shot Learning](https://mlsanity.com/paper/2204.14198/) (2022) - [Sigmoid Loss for Language Image Pre-Training](https://mlsanity.com/paper/2303.15343/) (2023) - [RT-2: Vision-Language-Action Models Transfer Web Knowledge to Robotic Control](https://mlsanity.com/paper/2307.15818/) (2023) - [OpenVLA: An Open-Source Vision-Language-Action Model](https://mlsanity.com/paper/2406.09246/) (2024) - [DINOv3](https://mlsanity.com/paper/2508.10104/) (2025) - [GR00T N1: An Open Foundation Model for Generalist Humanoid Robots](https://mlsanity.com/paper/2503.14734/) (2025) - [SigLIP 2: Multilingual Vision-Language Encoders with Improved Semantic Understanding, Localization, and Dense Features](https://mlsanity.com/paper/2502.14786/) (2025) - [Self-Supervised Learning from Images with a Joint-Embedding Predictive Architecture](https://mlsanity.com/paper/2301.08243/) (2023) - [Chameleon: Mixed-Modal Early-Fusion Foundation Models](https://mlsanity.com/paper/2405.09818/) (2024) - [Fine-Tuning Vision-Language-Action Models: Optimizing Speed and Success](https://mlsanity.com/paper/2502.19645/) (2025) - [V-JEPA 2: Self-Supervised Video Models Enable Understanding, Prediction and Planning](https://mlsanity.com/paper/2506.09985/) (2025) - [R3M: A Universal Visual Representation for Robot Manipulation](https://mlsanity.com/paper/2203.12601/) (2022) - [RDT-1B: a Diffusion Foundation Model for Bimanual Manipulation](https://mlsanity.com/paper/2410.07864/) (2024) - [Revisiting Feature Prediction for Learning Visual Representations from Video](https://mlsanity.com/paper/2404.08471/) (2024) - [Learning Universal Policies via Text-Guided Video Generation](https://mlsanity.com/paper/2302.00111/) (2023) - [SpatialVLA: Exploring Spatial Representations for Visual-Language-Action Model](https://mlsanity.com/paper/2501.15830/) (2025) - [RoboTwin 2.0: A Scalable Data Generator and Benchmark with Strong Domain Randomization for Robust Bimanual Robotic Manipulation](https://mlsanity.com/paper/2506.18088/) (2025) - [CoT-VLA: Visual Chain-of-Thought Reasoning for Vision-Language-Action Models](https://mlsanity.com/paper/2503.22020/) (2025) - [VIP: Towards Universal Visual Reward and Representation via Value-Implicit Pre-Training](https://mlsanity.com/paper/2210.00030/) (2022) - [WorldVLA: Towards Autoregressive Action World Model](https://mlsanity.com/paper/2506.21539/) (2025) - [LIBERO-Plus: In-depth Robustness Analysis of Vision-Language-Action Models](https://mlsanity.com/paper/2510.13626/) (2025) - [Masked Visual Pre-training for Motor Control](https://mlsanity.com/paper/2203.06173/) (2022) - [Where are we in the search for an Artificial Visual Cortex for Embodied Intelligence?](https://mlsanity.com/paper/2303.18240/) (2023) - [RoboTwin: Dual-Arm Robot Benchmark with Generative Digital Twins](https://mlsanity.com/paper/2504.13059/) (2025) - [Vision-Language-Action (VLA) Models: Concepts, Progress, Applications and Challenges](https://mlsanity.com/paper/2505.04769/) (2025) - [RynnVLA-002: A Unified Vision-Language-Action and World Model](https://mlsanity.com/paper/2511.17502/) (2025) ## Built on by (most cited first, 24 of 24) - [Being-H0.7: A Latent World-Action Model from Egocentric Videos](https://mlsanity.com/paper/2605.00078/) (2026) - [World Action Models: The Next Frontier in Embodied AI](https://mlsanity.com/paper/2605.12090/) (2026) - [World Model for Robot Learning: A Comprehensive Survey](https://mlsanity.com/paper/2605.00080/) (2026) - [JEPA-WAM: Learning Vision-Language-Action Policies with Joint-Embedding World Modeling](https://mlsanity.com/paper/2608.09381/) (2026) - [VLANeXt: Recipes for Building Strong VLA Models](https://mlsanity.com/paper/2602.18532/) (2026) - [LiLa-WAM: Lightweight Latent Reasoning World-Action Model for Robotic Manipulation](https://mlsanity.com/paper/2608.03701/) (2026) - [Generation Models Know Space: Unleashing Implicit 3D Priors for Scene Understanding](https://mlsanity.com/paper/2603.19235/) (2026) - [Orca: The World is in Your Mind](https://mlsanity.com/paper/2606.30534/) (2026) - [ω-EVA: Envision, Verify, and Act with Latent Interactive World Models](https://mlsanity.com/paper/2606.09457/) (2026) - [World Pilot: Steering Vision-Language-Action Models with World-Action Priors](https://mlsanity.com/paper/2606.12403/) (2026) - [VLAFlow: A Unified Training Framework for Vision-Language-Action Models via Co-training and Future Latent Alignment](https://mlsanity.com/paper/2607.01586/) (2026) - [PVI: Plug-in Visual Injection for Vision-Language-Action Models](https://mlsanity.com/paper/2603.12772/) (2026) - [CoEnv: Driving Embodied Multi-Agent Collaboration via Compositional Environment](https://mlsanity.com/paper/2604.05484/) (2026) - [UniMPA: A Unified Memory-Prediction-Action Model via Action-Grounded Transition Modeling](https://mlsanity.com/paper/2609.11875/) (2026) - [World Models for Embodied Intelligence: From Plausible to Controllable to Actionable](https://mlsanity.com/paper/2609.16697/) (2026) - [LeapBot-WA: World-Anchor Action Models via Predictive Latent Alignments](https://mlsanity.com/paper/2607.23969/) (2026) - [PAVE: Predictive Alignment and Value-Guided Evolution for World-Action Policies](https://mlsanity.com/paper/2608.30378/) (2026) - [JEPA Policy: Diffusion-Free Imitation Learning via Paired Action and Future Representation Prediction](https://mlsanity.com/paper/2609.09630/) (2026) - [Latent evolving World Action Model](https://mlsanity.com/paper/2609.27455/) (2026) - [Staircase Policy: Streaming Inference for World-Action Models with Large Action Chunks](https://mlsanity.com/paper/2609.36471/) (2026) - [Native Action-Prior Learning from Videos for World Action Models](https://mlsanity.com/paper/2610.03391/) (2026) - [Juno: Taming Predictive Latents for Vision-Language-Action Models](https://mlsanity.com/paper/2610.09940/) (2026) - [When Listening Becomes Easier: Scrubbing Visual Cues for Shortcut-Free VLAs](https://mlsanity.com/paper/2610.10912/) (2026) - [Vision-Predictor-Action: Towards Efficient Embodied AI via Neuro-Symbolic JEPA Predictors](https://mlsanity.com/paper/2610.15556/) (2026) ## Abstract (arXiv) Recent vision-language-action (VLA) models built upon pretrained vision-language models (VLMs) have achieved significant improvements in robotic manipulation. However, current VLAs still suffer from low sample efficiency and limited generalization. This paper argues that these limitations are closely tied to an overlooked component, pretrained visual representation, which offers insufficient knowledge on both aspects of environment understanding and policy prior. Through an in-depth analysis, we find that commonly used visual representations in VLAs, whether pretrained via language-image contrastive learning or image-based self-supervised learning, remain inadequate at capturing crucial, task-relevant environment information and at inducing effective policy priors, i.e., anticipatory knowledge of how the environment evolves under successful task execution. In contrast, we discover that predictive embeddings pretrained on videos, in particular V-JEPA 2, are adept at flexibly discarding unpredictable environment factors and encoding task-relevant temporal dynamics, thereby effectively compensating for key shortcomings of existing visual representations in VLAs. Building on these observations, we introduce JEPA-VLA, a simple yet effective approach that adaptively integrates predictive embeddings into existing VLAs. Our experiments demonstrate that JEPA-VLA yields substantial performance gains across a range of benchmarks, including LIBERO, LIBERO-plus, RoboTwin2.0, and real-robot tasks.