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DriftingVLA: Native One-Step Vision-Language-Action Generation via Per-Dimension Temporal Drifting

arXiv · AI, language, vision and robotics · article · Aug 30, 2026 · UTC

Conventional flow-based vision-language-action (VLA) models support expressive continuous action generation but rely on multi-step refinement to produce each action chunk, increasing latency in online robot control. To address this issue, we introduce DriftingVLA, a native one-step VLA that generates a complete action chunk with a single action-expert forward pass. Rather than learning a flow field that requires iterative integration at inference, DriftingVLA uses a distribution-drifting objective to learn a direct noise-to-action-chunk mapping for one-step deployment. Since robot action dimen

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