The groundbreaking V-JEPA 2 enables AI systems to grasp fundamental physical concepts that humans and animals develop naturally, such as gravity, object permanence, and cause-and-effect relationships12. This sophisticated model can predict physical outcomes like a ball falling when rolling off a table, or anticipate appropriate actions such as transferring cooked eggs from a pan to a plate when a robot holds relevant utensils near a stove23.

Trained on over one million hours of video and one million images, the model learns patterns of physical interaction without requiring additional human annotation14. This extensive dataset allows V-JEPA 2 to understand how people interact with objects, how objects move through space, and how different objects interact with each other - creating an internal simulation of reality that enables prediction and reasoning about physical interactions rather than simply reacting to immediate inputs56.

The two-stage training process employed by V-JEPA 2 distinguishes it from conventional AI models. Initially, self-supervised learning extracts patterns from vast video datasets without human labeling, followed by action-conditioned learning using approximately 62 hours of robot control data that enables the model to factor in agent actions when predicting outcomes.12 This sophisticated approach facilitates zero-shot planning and robot control in unfamiliar environments, allowing the system to operate effectively in previously unencountered situations.3

Performance benchmarks indicate that V-JEPA 2 operates 30 times faster than Nvidia's competing Cosmos model, though different evaluation metrics may be in use.24 Meta has also released three new benchmarks - IntPhys 2, MVPBench, and CausalVQA - to help researchers evaluate how well AI models learn and reason about physical phenomena using video, with current models including V-JEPA 2 still trailing human performance (95% accuracy) significantly.56

Laboratory testing has shown impressive results for robots equipped with V-JEPA 2, with success rates between 65% and 80% for pick-and-place tasks involving previously unseen objects.12 The system works by generating candidate actions, evaluating them based on predicted outcomes, and selecting the optimal move at each step.3 This approach enables robots to effectively "think before they act" rather than simply reacting to immediate inputs.4

For simpler tasks like basic pick-and-place operations, the system evaluates potential actions directly, while more complex challenges utilize a sequence of visual subgoals to guide behavior.25 This capability is particularly valuable for delivery robots and autonomous vehicles that must navigate unpredictable environments, as it allows them to understand physical principles rather than memorizing specific scenarios.6 The technology represents a crucial advancement toward Meta's goal of achieving advanced machine intelligence (AMI) - systems that can learn about the world as humans do and efficiently adapt to changing environments.7

The open-source availability of V-JEPA 2 is strategically designed to accelerate research progress across the AI community, aligning with Meta CEO Mark Zuckerberg's personal initiative to recruit experts and establish Meta as a leader in artificial general intelligence (AGI).12 According to Meta's Chief AI Scientist Yann LeCun, "world models will usher in a new era for robotics, enabling real world AI agents to help with chores and physical tasks without needing astronomical amounts of robotic training data."3

This technology represents a significant shift in AI development, as world models provide AI with human-like contextual understanding that traditional systems lack, paving the way for advancements in decision-making capabilities.4 Unlike language models that primarily process text based on linguistic patterns, these world models aim to create internal simulations of reality that enable prediction, planning, and reasoning about physical interactions—a crucial capability for AI systems that must deal with uncertainty in dynamic environments.567