Mixture of Horizons in Action Chunking 事件

Mixture of Horizons in Action Chunking arXiv:2511.19433v2 Announce Type: replace-cross Abstract: Vision-language-action (VLA) models have shown remarkable capabilities in robotic manipulation, but their performance is sensitive to the $\textbf{action chunk length}$ used during training, termed $\textbf{horizon}$. Our empirical study reveals an inherent trade-off: longer horizons provide stronger global foresight but degrade fine-grained accuracy, while shorter ones sharpen local control yet str