Author

Basile Terver

2 papers indexed here

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#machine learning Preprint Oct 2026

H-JEPA: End-to-End Learning of Hierarchical World Models for Visual Planning

H-JEPA is introduced, an end-to-end recipe for training a hierarchy of action-conditioned JEPAs in which each level predicts farther ahead in its own learned latent space in which each level's predictions become subgoals for the planner below it.

Wan-Cong Zhang, Basile Terver, Michael G. Rabbat et al. · 4 citations · ⚡2

#artificial intelligence Dec 2025

What Drives Success in Physical Planning with Joint-Embedding Predictive World Models?

This work characterize models from this family of methods as JEPA-WMs and investigates the technical choices that make algorithms from this class work, and proposes a model that outperforms two established baselines, DINO-WM and V-JEPA-2-AC, in both navigation and manipulation tasks.

Basile Terver, Tsung-Yen Yang, Jean Ponce et al. · Trans. Mach. Learn. Res. · 49 citations · ⚡7