让生成更简单：VLA 模型的一步动作生成

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Abstract:One-step generation from standard diffusion or Flow-Matching training is rarely effective for text-to-image or video generation without distillation or specialized one-step objectives. We find that VLA action generation behaves strikingly differently. Biasing only the training timestep distribution toward the noise endpoint yields strong one-step policies across LIBERO, LIBERO-Plus, LIBERO-Pro, and real-world robot tasks. Moreover, for open-source VLA models with different architectures, simply replacing iterative decoding with a single forward pass often performs comparably to the original multi-step decoding on benchmarks such as LIBERO, CALVIN, and SimplerEnv. We attribute this difference to the condition-target structure of VLA generation, which is closer to image-to-text than text-to-image: rich observations strongly constrain a comparatively compact target. We formalize this intuition through the irreducible velocity loss $R_v(t,c)$ of standard Flow Matching, which becomes much smaller toward the noise endpoint for VLA, whereas image-generation models usually exhibit a U-shaped profile. The geometry induced by the learned generative map shows the same contrast: the Jacobian of this map with respect to the initial noise has a substantially smaller largest singular value for action generation than for image generation. Together, these results suggest that VLA's condition-target structure makes one-step generation unusually accessible to standard Flow Matching. Code and training configurations for this paper are available at this https URL.

Submission history

From: Yitong Chen [view email]
[v1] Thu, 4 Jun 2026 05:58:30 UTC (482 KB)
[v2] Mon, 13 Jul 2026 05:29:03 UTC (1,959 KB)
[v3] Wed, 21 Oct 2026 10:31:33 UTC (3,056 KB)