VGAS: Value-Guided Action-Chunk Selection for Few-Shot Vision-Language-Action Adaptation

• URL: http://arxiv.org/abs/2602.07399v1
• Date: Sat, 07 Feb 2026 06:31:53 GMT
• Title: VGAS: Value-Guided Action-Chunk Selection for Few-Shot Vision-Language-Action Adaptation
• Authors: Changhua Xu, Jie Lu, Junyu Xuan, En Yu,
• Abstract summary: Vision--Language--Action (VLA) models bridge multimodal reasoning with physical control, but adapting them to new tasks with scarce demonstrations remains unreliable.<n>We study few-shot VLA adaptation from a emphgeneration--selection perspective and propose a novel framework textbfVGAS (textbfValue-textbfGuided textbfAction-chunk textbfSelection)<n>It performs inference-time best-of-$N$ selection to identify action chunks that are both semantic
• Score: 22.508129824741555
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Vision--Language--Action (VLA) models bridge multimodal reasoning with physical control, but adapting them to new tasks with scarce demonstrations remains unreliable. While fine-tuned VLA policies often produce semantically plausible trajectories, failures often arise from unresolved geometric ambiguities, where near-miss action candidates lead to divergent execution outcomes under limited supervision. We study few-shot VLA adaptation from a \emph{generation--selection} perspective and propose a novel framework \textbf{VGAS} (\textbf{V}alue-\textbf{G}uided \textbf{A}ction-chunk \textbf{S}election). It performs inference-time best-of-$N$ selection to identify action chunks that are both semantically faithful and geometrically precise. Specifically, \textbf{VGAS} employs a finetuned VLA as a high-recall proposal generator and introduces the \textrm{Q-Chunk-Former}, a geometrically grounded Transformer critic to resolve fine-grained geometric ambiguities. In addition, we propose \textit{Explicit Geometric Regularization} (\texttt{EGR}), which explicitly shapes a discriminative value landscape to preserve action ranking resolution among near-miss candidates while mitigating value instability under scarce supervision. Experiments and theoretical analysis demonstrate that \textbf{VGAS} consistently improves success rates and robustness under limited demonstrations and distribution shifts. Our code is available at https://github.com/Jyugo-15/VGAS.

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