VLA-ZO：視覚言語行動モデルのための高速ゼロ次適応
VLA-ZO: Fast Zeroth-Order Adaptation for Vision-Language-Action Models
VLAモデルの展開時の分布シフトに適応するため、行動側のみをゼロ次最適化で適応し、視覚言語部分を固定して計算を再利用することで高速化を実現した。
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著者: Jaemin Kim, Jiahn Kim, Taesik Gong
分類: cs.RO, cs.AI
原文アブストラクト
Adapting vision-language-action (VLA) models to deployment-time distribution shifts is important for reliable robotic operation, but conventional first-order adaptation can exceed the memory budget of inference-oriented deployment platforms. Zeroth-order (ZO) optimization offers a forward-only alternative with inference-level memory, but accurate gradient estimation requires many perturbation queries, making naive ZO prohibitively slow for large VLA models. We present VLA-ZO, a framework for fast ZO adaptation that exploits the structure of VLA computation. By confining adaptation to the action side, VLA-ZO keeps the expensive vision-language prefix frozen and reuses its conditioning states across perturbation queries and optimizer steps, while schedule-aware prefetching hides state-transfer overhead. On LIBERO camera-viewpoint shifts, VLA-ZO reduces end-to-end adaptation time by 25.59$\times$ at $q=16$ and 32.54$\times$ at $q=64$ relative to baseline ZO, while improving average task success from 48.27% without adaptation to 58.17% and 63.58%, respectively. These results show that making ZO faster can make larger query budgets practical, providing a promising path toward resource-efficient VLA adaptation on deployment platforms.