Action chunking enables Vision Language Action (VLA) models to run in real time, but naive chunked execution often exhibits discontinuities at chunk boundaries. Real-Time Chunking (RTC) alleviates this issue but is external to the policy, leading to spurious multimodal switching and trajectories that are not intrinsically smooth. We propose Legato, a training-time continuation method for action-chunked flow-based VLA policies. Specifically, Legato initializes denoising from a schedule-shaped mixture of known actions and noise, exposing the model to partial action information. Moreover, Legato reshapes the learned flow dynamics to ensure that the denoising process remains consistent between training and inference under per-step guidance. Legato further uses randomized schedule condition during training to support varying inference delays and achieve controllable smoothness. Empirically, Legato produces smoother trajectories and reduces spurious multimodal switching during execution, leading to less hesitation and shorter task completion time. Extensive real-world experiments show that Legato consistently outperforms RTC across five manipulation tasks, achieving approximately 10% improvements in both trajectory smoothness and task completion time.

翻译：动作分块使视觉语言动作（VLA）模型能够实时运行，但朴素的分块执行通常在分块边界处表现出不连续性。实时分块（RTC）缓解了这一问题，但其外在于策略，导致虚假的多模态切换以及本质上不平滑的轨迹。我们提出了Legato，一种用于基于流的动作分块VLA策略的训练时连续性方法。具体而言，Legato从已知动作与噪声按调度形状混合的状态初始化去噪过程，使模型接触部分动作信息。此外，Legato重塑学习到的流动态，以确保在每步指导下的训练与推理过程中，去噪过程保持一致。Legato在训练期间进一步使用随机化调度条件，以支持可变的推理延迟并实现可控的平滑度。实验表明，Legato在执行过程中产生更平滑的轨迹并减少虚假的多模态切换，从而减少犹豫并缩短任务完成时间。广泛的真实世界实验表明，在五项操作任务中，Legato始终优于RTC，在轨迹平滑度和任务完成时间上均实现了约10%的提升。