EfficientVLA: Training-Free Acceleration and Compression for
  Vision-Language-Action Models
Authors: name: Yantai Yang, name: Yuhao Wang, name: Zichen Wen, name: Luo Zhongwei, name: Chang Zou, name: Zhipeng Zhang, name: Chuan Wen, name: Linfeng Zhang

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.