Big new paper nobody is talking about ‼️ 🌎 Robot world models now have scaling laws. Meta trained an 8B-parameter JEPA on 15,000 hours of r…

Big new paper nobody is talking about ‼️ 🌎 Robot world models now have scaling laws. Meta trained an 8B-parameter JEPA on 15,000 hours of robot video to prove it. RoboJEPA (FAIR at Meta + Mila) 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%. Why it matters: SCALING LAWS. You can forecast what more compute buys before you spend it.

Source: Yann LeCun (X) | 2026-10-08

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