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arXiv · 2606.05737

Let It Be Simple: One-Step Action Generation for Vision-Language-Action Models

Yitong Chen·Shiduo Zhang·Jingjing Gong·Xipeng Qiu
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Abstract

Generating diverse images from sparse text is hard; generating compact actions from rich observations is easier. From the condition-target view, Vision-Language-Action (VLA) thus aligns with image-to-text, not text-to-image. We formalize this view through the irreducible velocity loss $R_v(t,c)$ of standard flow matching and validate it with a controlled 8-mode toy experiment and image-to-text MNIST task. We then show that high-noise training boosts one-step VLA decoding on standard LIBERO, achieving 95.6% on LIBERO-Long, and remains competitive across LIBERO-Plus, LIBERO-Pro, and real-world robot tasks, while ablations that weaken the condition or expand the horizon predictably erase the one-step gain. These results suggest that whether one-step action generation works in VLA depends not on specialized training, but on the condition-target structure.

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Yitong Chen, Shiduo Zhang, Jingjing Gong, Xipeng Qiu. 2026-07-13. Let It Be Simple: One-Step Action Generation for Vision-Language-Action Models. https://arxiv.org/abs/2606.05737

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