Bridging the Semantic-Action Gap in Visual Token Pruning for Efficient VLA Inference

Understand

Vision-Language-Action (VLA) models have shown great potential for embodied AI by integrating visual perception, language understanding, and action execution.

• In real-time deployment, these models must process continuous visual streams, incurring substantial computational overhead.
• Visual token pruning -- a mainstream technique for accelerating Vision-Language Models (VLMs) by retaining salient tokens while discarding redundant ones -- offers a natural candidate solution to this challenge.
• However, directly applying VLM-oriented pruning methods to VLA inference can cause severe degradation in manipulation performance.