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README.md

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 This repository hosts the official implementation of **Mixture of Horizons (MoH)**, introduced in the paper [Mixture of Horizons in Action Chunking](https://huggingface.co/papers/2511.19433).
 
-Vision-language-action (VLA) models for robotic manipulation are highly sensitive to the chosen **action chunk length**, or **horizon**. A fixed horizon presents an inherent trade-off: longer horizons offer superior global foresight but compromise fine-grained accuracy, while shorter ones provide precise local control but struggle with long-term tasks.
+Vision-language-action (VLA) models for robotic manipulation are highly sensitive to the chosen **action chunk length**, termed **horizon** in this work. A fixed horizon presents an inherent trade-off: longer horizons offer superior global foresight but compromise fine-grained accuracy, while shorter ones provide precise local control but struggle with long-term tasks.
 
 To address this challenge, we propose **Mixture of Horizons (MoH)**, a novel, plug-and-play strategy that fuses multiple horizons within a single policy. MoH processes action chunks in parallel segments with different horizons and integrates their outputs. This approach simultaneously leverages long-term foresight and short-term precision with minimal overhead, and enables **Dynamic Inference** through cross-horizon consensus for enhanced efficiency and robustness in complex robotic tasks.