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Task-specific visual token compression, not just saliency, unlocks substantial efficiency gains for VLA models without retraining.
These results establish recipe-dependent interactions and identify concrete precision assignments, rather than universal layer-sensitivity rules, rather than universal layer-sensitivity rules for vision-language-action models.