arXiv:2602.07399v2 Announce Type: replace 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 actions 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}u
The development of more reliable and adaptable VLA models is crucial as AI systems move towards more complex physical interactions and real-world deployment. Advances in few-shot learning and multimodal reasoning are maturing simultaneously.
Improving few-shot adaptation for Vision-Language-Action models will accelerate the deployment of intelligent agents in varied, unstructured environments, demanding less data for new tasks. This directly impacts the efficiency and scalability of AI applications requiring physical control.
The ability to more reliably adapt VLA models to new tasks with limited demonstrations means faster iteration and deployment cycles for autonomous systems. The proposed generation-selection framework addresses a key reliability bottleneck in VLA applications.
- · AI robotics developers
- · Logistics and manufacturing
- · Researchers in VLA models
- · Automation companies
- · Companies relying on extensive manual data labeling
- · Inefficient robot deployment methodologies
More robust and adaptable autonomous robots begin to appear in commercial settings, requiring less human intervention for new tasks.
The reduced need for large, task-specific datasets accelerates the development and deployment of a wider range of specialized AI agents.
This could lead to a proliferation of AI-driven physical agents in diverse sectors, fundamentally altering labor requirements and industrial processes over the next decade.
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Read at arXiv cs.AI