AI RESEARCH
LoopVLA: Learning Sufficiency in Recurrent Refinement for Vision-Language-Action Models
arXiv CS.AI
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ArXi:2605.09948v1 Announce Type: new Current Vision-Language-Action (VLA) models typically treat the deepest representation of a vision-language backbone as universally optimal for action prediction. However, robotic manipulation is composed of many frequent closed-loop spatial adjustments, for which excessive abstraction may waste computation and weaken low-level geometric cues essential for precise control.