LeCun, Yann

DINO-WM: World Models on Pre-trained Visual Features enable Zero-shot Planning

Zhou, G., Pan, H., LeCun, Y., & Pinto, L. (2024) · arXiv

Reinforcement Learning in RoboticsAI-based Problem Solving and Planning

Cited by 5

Intuitive physics understanding emerges from self-supervised pretraining on natural videos

Garrido, Q., Ballas, N., Assran, M., Bardes, A., Najman, L., Rabbat, M., Dupoux, E., & LeCun, Y. (2025) · arXiv

Child and Animal Learning DevelopmentMultimodal Machine Learning Applications

Cited by 3

Light-weight probing of unsupervised representations for reinforcement learning

Zhang, W., GX-Chen, A., Sobal, V., LeCun, Y., & Carion, N. (2022) · arXiv

Reinforcement Learning in RoboticsDomain Adaptation and Few-Shot Learning

Cited by 2

Learning from Reward-Free Offline Data: A Case for Planning with Latent Dynamics Models

Sobal, V., Zhang, W., Cho, K., Balestriero, R., Rudner, T. G. J., & LeCun, Y. (2025) · arXiv

Reinforcement Learning in RoboticsAI-based Problem Solving and Planning

Cited by 1

LeWorldModel: Stable End-to-End Joint-Embedding Predictive Architecture from Pixels

Maes, L., Lidec, Q. L., Scieur, D., LeCun, Y., & Balestriero, R. (2026) · arXiv

Reinforcement Learning in RoboticsAI-based Problem Solving and Planning

Cited by 0