VLA-ACL: Action-Consistent Visual Token Pruning for Efficien
Overview
Source roundup from published reports. Claims below are attributed to their publishers, not independently verified. arXiv Robotics — research abstracts: VLA-ACL: Action-Consistent Visual Token Pruning for Efficient Vision-Language-Action Models. VLA-ACL is a novel approach that prunes visual tokens in vision-language-action models using action-level supervision, while keeping the base model frozen. It achieves up to 87.5% token pruning, 75% computation reduction…
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Reported quantity · inference speedup: 1.5 other · Basis not reported
“s to remain consistent with the full-context teacher, with ground-truth actions as auxiliary supervision. This directly ties token selection to its effect on the downstream control output. Experiments on LIBERO and real-world manipulation tasks show that VLA-ACL prunes up to 87.5% of visual tokens while retaining competitive performance, reduces computation by up to 75%, and achieves a 1.5x inference speedup. These r”
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“esults establish a stronger performance-efficiency trade-off than existing frozen-VLA pruning methods and demonstrate the value of action-level supervision for visual token selection. Code is available at https://github.com/du-owen/VLA-ACL.”
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- arXiv Robotics — research abstractsVLA-ACL: Action-Consistent Visual Token Pruning for Efficient Vision-Language-Action Models
VLA-ACL is a novel approach that prunes visual tokens in vision-language-action models using action-level supervision, while keeping the base model frozen. It achieves up to 87.5% token pruning, 75% computation reduction, and a 1.5x inference speedup on LIBERO and real-world tasks.
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