Robot world models now have scaling laws. Meta trained an 8B-parameter JEPA on 15,000 hours of robot video to prove it.

RoboJEPA imagines the future in V-JEPA 2.1's feature space, then plans toward a single goal image. The authors call it the largest JEPA predictor trained to date.

Trained on 23 public datasets: 12 robot embodiments, 15,022 hours of video, 6,692 of them with actions.

Fit the scaling law on 22M–2B models and it accurately predicts the 4B and 8B results.

Skills unlock in order as compute grows: moving the arm, holding an object, avoiding obstacles, and only past ~10^22 FLOPs, pushing objects.

On a real Franka with no task fine-tuning, the 8B model grasps 67% of the time vs 5% for π0.5. Not apples-to-apples (goal image vs text), and π0.5 still wins pick-and-place, 53% vs 27%.