Olaf-World: Orienting Latent Actions for Video World Modeling
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.
698bfbeb6052d3bed9630ae1
Yuxin Jiang
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2026-02-11T11:14:13.438Z
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Jiang Yuxin
YuxinJ
user
YuxinJ
698bfbeb6052d3bed9630ae2
Yuchao Gu
698bfbeb6052d3bed9630ae3
Ivor W. Tsang
698bfbeb6052d3bed9630ae4
Mike Zheng Shou
Olaf-World: Orienting Latent Actions for Video World Modeling
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.
Sequence-level control-effect alignment enables structured latent action space learning for zero-shot action transfer in video world models.
action-controllable world models latent action learning temporal feature differences self-supervised video encoder sequence-level control-effect alignment action-conditioned video world models zero-shot action transfer data-efficient adaptation