EfficientVLA: Training-Free Acceleration and Compression for
  Vision-Language-Action Models
Vision-Language-Action (VLA) models, particularly diffusion-based
architectures, demonstrate transformative potential for embodied intelligence
but are severely hampered by high computational and memory demands stemming
from extensive inherent and inference-time redundancies. While existing
acceleration efforts often target isolated inefficiencies, such piecemeal
solutions typically fail to holistically address the varied computational and
memory bottlenecks across the entire VLA pipeline, thereby limiting practical
deployability. We introduce EfficientVLA, a structured and training-free
inference acceleration framework that systematically eliminates these barriers
by cohesively exploiting multifaceted redundancies. EfficientVLA
synergistically integrates three targeted strategies: (1) pruning of
functionally inconsequential layers from the language module, guided by an
analysis of inter-layer redundancies; (2) optimizing the visual processing
pathway through a task-aware strategy that selects a compact, diverse set of
visual tokens, balancing task-criticality with informational coverage; and (3)
alleviating temporal computational redundancy within the iterative
diffusion-based action head by strategically caching and reusing key
intermediate features. We apply our method to a standard VLA model CogACT,
yielding a 1.93X inference speedup and reduces FLOPs to 28.9%, with only a 0.6%
success rate drop in the SIMPLER benchmark.
68522b190164cd13167104d9
Yantai Yang
claimed_verified
2025-07-07T11:25:45.011Z
65b9f710e7c83813628a5cd0
/avatars/94223a01fb8461eef6a3ac5c703be436.svg
yyt
user
hiteryyt
68522b190164cd13167104da
Yuhao Wang
68522b190164cd13167104db
Zichen Wen
claimed_verified
2025-06-23T08:20:39.932Z
653b8c3e97a4d71d950e2f20
/avatars/b68880022e14556d0be58c69615db3be.svg
Zichen Wen
user
zichenwen
68522b190164cd13167104dc
Luo Zhongwei
68522b190164cd13167104dd
Chang Zou
claimed_verified
2025-07-21T08:30:41.617Z
6623417a9c9b00aaff64de33
/avatars/58c108ebf486d90866d8626bbb69312d.svg
Chang Zou
user
Shenyizou
68522b190164cd13167104de
Zhipeng Zhang
68522b190164cd13167104df
Chuan Wen
68522b190164cd13167104e0
Linfeng Zhang
EfficientVLA: Training-Free Acceleration and Compression for
  Vision-Language-Action Models
Vision-Language-Action (VLA) models, particularly diffusion-based
architectures, demonstrate transformative potential for embodied intelligence
but are severely hampered by high computational and memory demands stemming
from extensive inherent and inference-time redundancies. While existing
acceleration efforts often target isolated inefficiencies, such piecemeal
solutions typically fail to holistically address the varied computational and
memory bottlenecks across the entire VLA pipeline, thereby limiting practical
deployability. We introduce EfficientVLA, a structured and training-free
inference acceleration framework that systematically eliminates these barriers
by cohesively exploiting multifaceted redundancies. EfficientVLA
synergistically integrates three targeted strategies: (1) pruning of
functionally inconsequential layers from the language module, guided by an
analysis of inter-layer redundancies; (2) optimizing the visual processing
pathway through a task-aware strategy that selects a compact, diverse set of
visual tokens, balancing task-criticality with informational coverage; and (3)
alleviating temporal computational redundancy within the iterative
diffusion-based action head by strategically caching and reusing key
intermediate features. We apply our method to a standard VLA model CogACT,
yielding a 1.93X inference speedup and reduces FLOPs to 28.9%, with only a 0.6%
success rate drop in the SIMPLER benchmark.
EfficientVLA accelerates Vision-Language-Action models by pruning language layers, optimizing visual token selection, and caching intermediate features in the diffusion-based action head.
diffusion-based architectures inference acceleration framework pruning inter-layer redundancies visual tokens task-aware strategy iterative diffusion-based action head caching FLOPs SIMPLER benchmark