Scaling Vision-Language-Action Models with Video Generative Priors

Scaling vision-language-action (VLA) models requires substantial robot data and computation. Pretrained video generators provide visual and temporal representations, but rendering future frames adds iterative sampling and re-encoding. We introduce VID-Policy, a video-generation-model-enhanced policy that transfers history-conditioned representations from a frozen video generator into a pretrained VLA. A projector maps these features to policy-side keys and values; gated additive attention combines them with native context while preserving the action generator. We assess source precision, history content, and closed-loop control in two policy families. In a fixed-seed, 24-task, five-episode-per-task DIAL development screen, BF16 video features yield 80.8% success, 4.2 percentage points above our reproduced Native DIAL reference. On the full 24-task, 50-episode-per-task RoboCasa-GR1-Tabletop benchmark, WALA-based VID-Policy reaches 72.33% success, improving over our WALA re-implementation by about 1.83 percentage points. The results show that video representations can guide action without sampling future frames.