# Egocentric Pretraining Scaling Law

Open-HumanScale scales egocentric human manipulation video pretraining to 50,000 hours and releases the corpus, training pipelines and checkpoints. On a unified world-action model, pretraining hours and downstream robot task success follow an empirical log-linear relationship, and the curve steepens when task and scene diversity grow alongside duration. Pretrained on human data alone, HumanWAM-Vanilla reaches 92.08% on RoboTwin 2.0 Clean2Clean and 68.88% on the randomized Clean2Random setting.

Open-HumanScale scales egocentric human manipulation video pretraining to 50,000 hours and releases the corpus, training pipelines and checkpoints. On a unified world-action model, pretraining hours and downstream robot task success follow an empirical log-linear relationship, and the curve steepens when task and scene diversity grow alongside duration. Pretrained on human data alone, HumanWAM-Vanilla reaches 92.08% on RoboTwin 2.0 Clean2Clean and 68.88% on the randomized Clean2Random setting.