VLA-Adapter: An Effective Paradigm for Tiny-Scale Vision-Language-Action
  Model
Vision-Language-Action (VLA) models typically bridge the gap between
perceptual and action spaces by pre-training a large-scale Vision-Language
Model (VLM) on robotic data. While this approach greatly enhances performance,
it also incurs significant training costs. In this paper, we investigate how to
effectively bridge vision-language (VL) representations to action (A). We
introduce VLA-Adapter, a novel paradigm designed to reduce the reliance of VLA
models on large-scale VLMs and extensive pre-training. To this end, we first
systematically analyze the effectiveness of various VL conditions and present
key findings on which conditions are essential for bridging perception and
action spaces. Based on these insights, we propose a lightweight Policy module
with Bridge Attention, which autonomously injects the optimal condition into
the action space. In this way, our method achieves high performance using only
a 0.5B-parameter backbone, without any robotic data pre-training. Extensive
experiments on both simulated and real-world robotic benchmarks demonstrate
that VLA-Adapter not only achieves state-of-the-art level performance, but also
offers the fast inference speed reported to date. Furthermore, thanks to the
proposed advanced bridging paradigm, VLA-Adapter enables the training of a
powerful VLA model in just 8 hours on a single consumer-grade GPU, greatly
lowering the barrier to deploying the VLA model. Project page:
https://vla-adapter.github.io/.
68c3cc49fc1747b912403b06
Yihao Wang
claimed_verified
2025-09-13T14:56:18.292Z
68c429ef60f075cc46cc9cff
/avatars/21d277a1b46eabc967121a111e270cdd.svg
yh-wang
user
yh-wang
68c3cc49fc1747b912403b07
Pengxiang Ding
68c3cc49fc1747b912403b08
Lingxiao Li
68c3cc49fc1747b912403b09
Can Cui
68c3cc49fc1747b912403b0a
Zirui Ge
68c3cc49fc1747b912403b0b
Xinyang Tong
68c3cc49fc1747b912403b0c
Wenxuan Song
claimed_verified
2025-09-15T15:08:36.302Z
66a0a3405c5e2a42a214c70f
/avatars/52b9ee7f899ee5431ed37fd1db378d9e.svg
Wenxuan Song
user
Wenxuan123
68c3cc49fc1747b912403b0d
Han Zhao
68c3cc49fc1747b912403b0e
Wei Zhao
68c3cc49fc1747b912403b0f
Pengxu Hou
68c3cc49fc1747b912403b10
Siteng Huang
68c3cc49fc1747b912403b11
Yifan Tang
68c3cc49fc1747b912403b12
Wenhui Wang
68c3cc49fc1747b912403b13
Ru Zhang
68c3cc49fc1747b912403b14
Jianyi Liu
68c3cc49fc1747b912403b15
Donglin Wang
VLA-Adapter: An Effective Paradigm for Tiny-Scale Vision-Language-Action
  Model
Vision-Language-Action (VLA) models typically bridge the gap between
perceptual and action spaces by pre-training a large-scale Vision-Language
Model (VLM) on robotic data. While this approach greatly enhances performance,
it also incurs significant training costs. In this paper, we investigate how to
effectively bridge vision-language (VL) representations to action (A). We
introduce VLA-Adapter, a novel paradigm designed to reduce the reliance of VLA
models on large-scale VLMs and extensive pre-training. To this end, we first
systematically analyze the effectiveness of various VL conditions and present
key findings on which conditions are essential for bridging perception and
action spaces. Based on these insights, we propose a lightweight Policy module
with Bridge Attention, which autonomously injects the optimal condition into
the action space. In this way, our method achieves high performance using only
a 0.5B-parameter backbone, without any robotic data pre-training. Extensive
experiments on both simulated and real-world robotic benchmarks demonstrate
that VLA-Adapter not only achieves state-of-the-art level performance, but also
offers the fast inference speed reported to date. Furthermore, thanks to the
proposed advanced bridging paradigm, VLA-Adapter enables the training of a
powerful VLA model in just 8 hours on a single consumer-grade GPU, greatly
lowering the barrier to deploying the VLA model. Project page:
https://vla-adapter.github.io/.
VLA-Adapter reduces reliance on large-scale VLMs and extensive pre-training by using a lightweight Policy module with Bridge Attention, achieving state-of-the-art performance and fast inference speed with minimal computational resources.
VLA models Vision-Language Model (VLM) robotic data VL conditions Policy module Bridge Attention parameter backbone simulated benchmarks real-world benchmarks inference speed consumer-grade GPU