A Survey on Efficient Vision-Language-Action Models
Authors: name: Zhaoshu Yu, name: Bo Wang, name: Pengpeng Zeng, name: Haonan Zhang, name: Ji Zhang, name: Lianli Gao, name: Jingkuan Song, name: Nicu Sebe, name: Heng Tao Shen
Organization: Tongji Unversity

Vision-Language-Action models (VLAs) represent a significant frontier in
embodied intelligence, aiming to bridge digital knowledge with physical-world
interaction. While these models have demonstrated remarkable generalist
capabilities, their deployment is severely hampered by the substantial
computational and data requirements inherent to their underlying large-scale
foundation models. Motivated by the urgent need to address these challenges,
this survey presents the first comprehensive review of Efficient
Vision-Language-Action models (Efficient VLAs) across the entire
data-model-training process. Specifically, we introduce a unified taxonomy to
systematically organize the disparate efforts in this domain, categorizing
current techniques into three core pillars: (1) Efficient Model Design,
focusing on efficient architectures and model compression; (2) Efficient
Training, which reduces computational burdens during model learning; and (3)
Efficient Data Collection, which addresses the bottlenecks in acquiring and
utilizing robotic data. Through a critical review of state-of-the-art methods
within this framework, this survey not only establishes a foundational
reference for the community but also summarizes representative applications,
delineates key challenges, and charts a roadmap for future research. We
maintain a continuously updated project page to track our latest developments:
https://evla-survey.github.io/