SpecPrune-VLA: Accelerating Vision-Language-Action Models via Action-Aware Self-Speculative Pruning

Understand

Pruning is a typical acceleration technique for compute-bound models by removing computation on unimportant values.

• Recently, it has been applied to accelerate Vision-Language-Action (VLA) model inference.
• However, existing acceleration methods focus on local information from the current action step and ignore the global context, leading to >20% success rate drop and limited speedup in some scenarios.
• In this paper, we point out spatial-temporal consistency in VLA tasks: input images in consecutive steps exhibit high similarity, and propose the key insight that token selection should combine local information with global context of the model.