Olaf-World: Orienting Latent Actions for Video World Modeling

Jiang Yuxin, Yuchao Gu, Ivor W. Tsang, Mike Zheng Shou

Olaf-World: Orienting Latent Actions for Video World Modeling: 28 upvotes on Hugging Face Daily Papers, #11 of 57 papers on 2026-02-11. Day-by-day upvote history.

Scaling action-controllable world models is limited by the scarcity of action labels. While latent action learning promises to extract control interfaces from unlabeled video, learned latents often fail to transfer across contexts: they entangle scene-specific cues and lack a shared coordinate system. This occurs because standard objectives operate only within each clip, providing no mechanism to align action semantics across contexts. Our key insight is that although actions are unobserved, their semantic effects are observable and can serve as a shared reference. We introduce SeqΔ-REPA, a sequence-level control-effect alignment objective that anchors integrated latent action to temporal feature differences from a frozen, self-supervised video encoder. Building on this, we present Olaf-World, a pipeline that pretrains action-conditioned video world models from large-scale passive video. Extensive experiments demonstrate that our method learns a more structured latent action space, leading to stronger zero-shot action transfer and more data-efficient adaptation to new control interfaces than state-of-the-art baselines.

Paper page on Hugging Face · arXiv

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