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 struggle on long-term tasks, implying fixed choice of single horizons being suboptimal. To mitigate the trade-off, we propose a $\textbf{mixtur
The rapid advancement in vision-language-action (VLA) models for robotics is hitting practical limitations, making action chunking optimization a crucial next step for real-world deployment.
Optimizing action chunk length directly improves the performance and versatility of VLA models in robotic manipulation, accelerating the path to advanced autonomous systems.
Robot learning frameworks will likely incorporate dynamic or adaptive 'horizon' selection, moving beyond static configurations and enhancing task adaptability.
- · Robotics companies
- · AI research institutions
- · Automation sector
- · Developers relying solely on fixed action chunk lengths
Improved performance and reliability of VLA models in complex robotic tasks.
Faster commercialization and broader adoption of AI-powered robotic systems across various industries.
Enhanced AI capability contributes to the development of more general-purpose humanoid robots and autonomous agents.
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Read at arXiv cs.AI