VLA Knows Its Limits

Action chunking has recently emerged as a standard prac-tice in flow-based Vision-Language-Action (VLA) models. However, thee!ect and choice of the execution horizon—the number of actions tobe executed from each predicted chunk—remains underexplored. In thiswork, we first show that varying the execution horizon leads to sub-stantial performance deviations, with performance initially improvingand then declining as the horizon increases. To uncover the reasons, weanalyze the cross- and self-attention weights in flow-based VLAs and re-veal two key phenomena: (i) intra-chunk actions attend invariantly tovision–language tokens, limiting adaptability to environmental changes;and (ii) the initial and terminal action tokens serve as stable anchors,forming latent centers around which intermediate actions are organized.Motivated by these insights, we interpret action self-attention weights asa proxy for the model’s predictive limit and propose AutoHorizon, thefirst test-time method that dynamically estimates the execution horizonfor each predicted action chunk to adapt to changing perceptual condi-tions. Across simulated and real-world robotic manipulation tasks, Au-toHorizon is performant, incurs negligible computational overhead, andgeneralizes across diverse tasks and flow-based models. Demonstrationvideos are available at this project page.