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Routing action-relevant VLA tokens across layers unlocks dramatic sample efficiency gains for robotic manipulation RL.
Visual token pruning often degrades robotic control because it ignores language conditioning; steering token cache reuse via text-vision entropy differentials cuts VLA compute by 2.45x while boosting task success by up to 14.5%.
