A Survey on Vision-Language-Action Models: An Action Tokenization
  Perspective
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.
686600cf8c83dab5f72d1ed0
Yifan Zhong
686600cf8c83dab5f72d1ed1
Fengshuo Bai
686600cf8c83dab5f72d1ed2
Shaofei Cai
claimed_verified
2025-07-03T07:14:49.923Z
6578459d62d3ac1817ed79fe
https://cdn-avatars.huggingface.co/v1/production/uploads/6578459d62d3ac1817ed79fe/AXDJuwLUoEOb4Fj3U0Xxo.jpeg
Shaofei Cai
user
phython96
686600cf8c83dab5f72d1ed3
Xuchuan Huang
claimed_verified
2025-07-03T07:14:52.141Z
66ee5cf1801ea45d7a44a542
/avatars/04bafbcbf1aea3920a79bddbd1a18f42.svg
XUCHUAN HUANG
user
Feernnn
686600cf8c83dab5f72d1ed4
Zhang Chen
686600cf8c83dab5f72d1ed5
Xiaowei Zhang
686600cf8c83dab5f72d1ed6
Yuanfei Wang
686600cf8c83dab5f72d1ed7
Shaoyang Guo
686600cf8c83dab5f72d1ed8
Tianrui Guan
686600cf8c83dab5f72d1ed9
Ka Nam Lui
686600cf8c83dab5f72d1eda
Zhiquan Qi
686600cf8c83dab5f72d1edb
Yitao Liang
686600cf8c83dab5f72d1edc
Yuanpei Chen
686600cf8c83dab5f72d1edd
Yaodong Yang
A Survey on Vision-Language-Action Models: An Action Tokenization
  Perspective
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.
This survey categorizes and analyzes vision-language-action models through the lens of action tokenization, identifying strengths, limitations, and future directions.
vision-language-action models VLA modules action tokens language description affordance trajectory goal state latent representation raw action reasoning