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  "text": "RT @LeoKharon: NEW WORLD MODEL: @ylecun's team is back with an efficient model!\n\nThis project involves @ylecun, @lukaskuhn77, @lucasmaes_,…",
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    "text": "NEW WORLD MODEL: @ylecun's team is back with an efficient model!\n\nThis project involves @ylecun, @lukaskuhn77, @lucasmaes_, @quentinlldc, and @randall_balestr.\n\nA couple definitions first:\n- DINO: self-DIstillation with NO labels. A self-supervised image model (Meta, 2021) where a student network learns to match a teacher (an EMA copy of itself) across two crops of the same image, with no labels and no negatives.\n- SIGReg: a regularizer that prevents embedding collapse by forcing the embeddings to match an isotropic Gaussian, tested with a normality test (Epps–Pulley) on many random 1-D projections instead of in full dimension.\n\nLeVJEPA is a self-supervised video pretraining method, released with open code, weights, and checkpoints.\nIt learns a video representation by pushing the embeddings of global and local crops of the same clip together (an invariance loss), while a regularizer called SIGReg forces the embeddings toward an isotropic Gaussian to provably prevent representation collapse.\n\nUnlike V-JEPA and V-JEPA 2 it uses a single shared encoder with a projector and no target network, no predictor and no stop-gradient.\nIt drops 95% of tokens per view, uses block-causal attention (each frame attends only to past frames), and has a single loss weight.\n\nIt is evaluated purely as a representation learner via frozen probing on ImageNet-1K, Something-Something-v2 and Kinetics-400, not on any robot.\n\nWhat I find interesting, is that V-JEPA and V-JEPA 2 need an EMA target encoder, stop-gradients and a capacity-limited predictor to avoid collapse; LeVJEPA drops all of it for one shared encoder plus projector, preventing collapse instead with the SIGReg regularizer under a provable guarantee and a single hyperparameter.\nThe \"P\" (predictor) in JEPA is effectively gone.\n\nLeVJEPA is also less compute intensive:\n- 5.6x to 20.8x lower total pretraining compute than V-JEPA 2\n- 7.6 points higher on ImageNet-1K at matched FLOPs\n- trains at batch size 128 within 8GB where V-JEPA 2 saturates at batch size 28\n\nAlso worth mentioning: ImageNet-1K accuracy rises monotonically with the token-drop rate, from 33.9% at rho = 0 to 47.6% at rho = 0.95.\nThe aggressive dropping is actually doing regularization work.\n\nOn the JEPA-versus-DINO debate:\n- it loses to DINOv2 by 3.1 points on ImageNet-1K (appearance, static)\n- but wins on Something-Something-v2 by nearly 2x (motion, temporal)\n- and beats V-JEPA 2 by 1.9 points on ViT-L at 5.6x lower cost.\n-> optimized for temporal and motion understanding per compute dollar.",
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