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ARXIV:2602.10104 · VIDEO WORLD MODELS · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE
ARXIV:2602.10104VIDEO WORLD MODELSSUBMITTED 02 APR · 02:30 UTCFRESHNESS STALEarXiv
Pretrain action-conditioned video world models for zero-shot action transfer and efficient adaptation.
Opportunity summary
Pain Pretrain action-conditioned video world models for zero-shot action transfer and efficient adaptation.
Evidence 0 refs | 0 sources | 17% coverage
Blocker Evidence unverified
Pretrain action-conditioned video world models for zero-shot action transfer and efficient adaptation. While latent action learning promises to extract control interfaces from unlabeled video, learned latents often fail to transfer across contexts: they entangle…
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…
ScienceToStartup currently rates this 6.0/10 on the public viability pass. 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…
Video World Models moved forward this cycle; last verified April 2026. Public score 6.0/10.
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Pretrain action-conditioned video world models for zero-shot action transfer and efficient adaptation.
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10.48550/arXiv.2602.10104Pretrain action-conditioned video world models for zero-shot action transfer and efficient adaptation.
Abstract
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 Seq$Δ$-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.
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PROBLEM
Pretrain action-conditioned video world models for zero-shot action transfer and efficient adaptation. While latent action learning promises to extract control interfaces from unlabeled video, learned latents often fail to transfer across contexts: they entangle scene-specific c...
METHOD
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 sh...
RESULT
ScienceToStartup currently rates this 6.0/10 on the public viability pass. 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 t...
WHY NOW
Video World Models moved forward this cycle; last verified April 2026. Public score 6.0/10.
Abstract-backed public claims while anchored extraction refreshes.
Pretrain action-conditioned video world models for zero-shot action transfer and efficient adaptation. 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.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 6.0/10 on the public viability pass. 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.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Video World Models moved forward this cycle; last verified April 2026. Public score 6.0/10.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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Pretrain action-conditioned video world models for zero-shot action transfer and efficient adaptation.
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Video World Models
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