AI RESEARCH
Dynamic Execution Commitment of Vision-Language-Action Models
arXiv CS.CV
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ArXi:2605.11567v1 Announce Type: new Vision-Language-Action (VLA) models predominantly adopt action chunking, i.e., predicting and committing to a short horizon of consecutive low-level actions in a single forward pass, to amortize the inference cost of large-scale backbones and reduce per-step latency. However, committing these multi-step predictions to real-world execution requires balancing success rate against inference efficiency, a decision typically governed by fixed execution horizons tuned per task.