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

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.