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arXiv AI By Zijian Jin, Yunbei Zhang, Yuanzhe Liu, Ming Liu, Baian Chen, Weirui Ye, Shilong Liu, Marco Pavone

DeepJEPA: Scaling World Models from Within

Read the original on arXiv AI →

DeepJEPA is a weight‑tied joint‑embedding predictive world model that treats transition depth as an inner test‑time scaling axis, learning when additional recurrent updates are worthwhile for each candidate and rollout step. Unlike traditional planners that uniformly deepen every transition, DeepJEPA concentrates extra computation on decision‑critical events such as contact onset and sustained object interaction, achieving comparable or better performance with only 1.00–1.26 updates per transition across five visual‑control settings. The approach demonstrates that improved planning does not require uniformly better object‑state decodability, but rather targeted internal computation where it can alter the planner’s elite set and action selection.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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