ActPrune: Action-Grounded Visual Token Pruning for Efficient Vision-Language-Action Models

ActPrune proposes a training-free visual token pruning method that moves the pruning decision ahead of the vision encoder and uses the action chunk predicted at the previous control step as a geometric prior. In the fixed third-person view, the method retains image patches near the projected end-effector trajectory along with semantic anchors; in the wrist view, it prunes patches that remain unchanged after compensating for action-induced camera motion.