EfficientVLA: Training-Free Acceleration and Compression for Vision-Language-Action Models

TL;DR

EfficientVLA introduces a holistic, training-free framework that reduces computational costs of vision-language-action models by pruning, optimizing visual tokens, and caching features, enabling faster inference with minimal accuracy loss.

Contribution

It presents a novel, comprehensive approach to accelerate VLA models without retraining, addressing multiple bottlenecks simultaneously.

Findings

Achieves 1.93X inference speedup on CogACT

Reduces FLOPs to 28.9% of original

Only 0.6% success rate drop in benchmark

Abstract

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.