A Survey on Vision-Language-Action Models: An Action Tokenization
  Perspective
Authors: name: Yifan Zhong, name: Fengshuo Bai, name: Shaofei Cai, name: Xuchuan Huang, name: Zhang Chen, name: Xiaowei Zhang, name: Yuanfei Wang, name: Shaoyang Guo, name: Tianrui Guan, name: Ka Nam Lui, name: Zhiquan Qi, name: Yitao Liang, name: Yuanpei Chen, name: Yaodong Yang

The remarkable advancements of vision and language foundation models in
multimodal understanding, reasoning, and generation has sparked growing efforts
to extend such intelligence to the physical world, fueling the flourishing of
vision-language-action (VLA) models. Despite seemingly diverse approaches, we
observe that current VLA models can be unified under a single framework: vision
and language inputs are processed by a series of VLA modules, producing a chain
of action tokens that progressively encode more grounded and
actionable information, ultimately generating executable actions. We further
determine that the primary design choice distinguishing VLA models lies in how
action tokens are formulated, which can be categorized into language
description, code, affordance, trajectory, goal state, latent representation,
raw action, and reasoning. However, there remains a lack of comprehensive
understanding regarding action tokens, significantly impeding effective VLA
development and obscuring future directions. Therefore, this survey aims to
categorize and interpret existing VLA research through the lens of action
tokenization, distill the strengths and limitations of each token type, and
identify areas for improvement. Through this systematic review and analysis, we
offer a synthesized outlook on the broader evolution of VLA models, highlight
underexplored yet promising directions, and contribute guidance for future
research, hoping to bring the field closer to general-purpose intelligence.