VLA-ACL: Can Token Pruning Speed Up Robot Manipulation?
Can a VLA discard most visual tokens without changing what the robot does? This episode examines VLA-ACL's action-supervised pruning method and separates its demonstrated compression benefits from its deployment assumptions. We cover three questions: why the larger token budget is the more convincing reliability trade-off; how preserving rotary positions changes the strength of attention-based baselines; and why a language-backbone speedup is not automatically a camera-to-command speedup. The method trains a separate selector while keeping the task-adapted VLA frozen, using both teacher…
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