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Routing action-relevant VLA tokens across layers unlocks dramatic sample efficiency gains for robotic manipulation RL.
VLAConf offers a computationally cheap, architecture-agnostic way to estimate task success confidence for robot manipulation by turning frozen VLA representations into anomaly scores.