SIEVE：面向VLA模型模仿学习的结构感知数据选择

摘要

视觉-语言-动作（VLA）模型通常通过在大规模机器人演示数据集上进行模仿学习来训练，但由于数据存在冗余、噪声和覆盖不均的问题，更多的数据并不一定能带来更好的策略。现有的数据选择方法往往在轨迹级或状态-动作级评估演示，忽略了构成长期行为的可复用结构。本文提出 SIEVE，一种面向 VLA 模仿学习的结构感知数据选择方法。SIEVE 将演示视为可复用基元与过渡接口的组合。它首先从分段轨迹中提取视觉运动基元，然后在递减回报的约束下，通过最大化复用感知的结构暴露来为组合模式分配选择预算。最后，它在每个组合模式分组内选取中心轨迹，以保留稳定、居中且适合模仿的演示。在多个数据集、基准测试和 VLA 模型上的实验表明，SIEVE 始终优于竞争性的数据选择基线方法。值得注意的是，仅使用 50% 的演示数据和 50% 的训练步数，SIEVE 即可超越全数据训练的性能，这表明通过基元和过渡捕获的可复用结构是高效 VLA 模仿学习的重要信号。

English

Vision-Language-Action (VLA) models are typically trained by imitation learning on large-scale robot demonstration datasets, but more data does not necessarily yield better policies due to redundancy, noise, and uneven coverage. Existing data selection methods often assess demonstrations at either the trajectory or state-action level, missing the reusable structures that compose long-horizon behaviors. In this paper, we propose SIEVE, a structure-aware data selection method for VLA imitation learning. SIEVE views demonstrations as compositions of reusable primitives and transition interfaces. It first discovers visuo-motor primitives from segmented trajectories, then allocates selection budgets to composition patterns by maximizing reuse-aware structural exposure under diminishing returns. Finally, it selects medoid trajectories within each composition-pattern bucket to retain central, stable, and imitation-friendly demonstrations. Experiments across multiple datasets, benchmarks, and VLA models show that SIEVE consistently outperforms competitive data selection baselines. Notably, SIEVE can surpass full-data training while using only 50% of demonstrations and 50% of training steps, suggesting that reusable structure, captured through primitives and transitions, is an important signal for efficient VLA imitation learning.