Author

Ruixiang Wang

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Preprint Aug 2026

Vid2WAM: Distilling Video Diffusion Priors into World Action Models

Vid2WAM is proposed, an offline distillation framework that transfers visual diffusion priors from a large video foundation model into a compact WAM student and introduces source-aware residual action adaptation that learns source-specific corrections around a shared action backbone and mitigates interference from noisy pseudo-actions.

Chenhao Qiu, Ruixiang Wang, Runyi Zhao et al. · 0 citations