Sunghwan Han

AdaVLA: Adaptive Step Flow Matching for Training-free Acceleration of Vision-Language-Action Models

A novel metric derived from the flow matching trajectory curvature is introduced to quantify action generation confidence during inference and enables the dynamic reduction of inference steps and the adaptive adjustment of MLP pruning ratios through an efficiently computed importance evaluation, requiring no access to training data.