Mixture of Horizons in Action Chunking
Vision-language-action (VLA) models have shown remarkable capabilities in robotic manipulation, but their performance is sensitive to the action chunk length used during training, termed 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 mixture of horizons (MoH) strategy. MoH rearranges the action chunk into several segments with different horizons, processes them in parallel with a shared action transformer, and fuses outputs with a light linear gate. It has three appealing benefits. 1) MoH exploits long-term foresight and short-term precision jointly within a single model, improving both performance and generalizability to complex tasks. 2) MoH is plug-and-play for full-attention action modules with minimal training or inference overhead. 3) MoH enables dynamic inference with adaptive horizons, which selects stable actions through cross-horizon consensus, achieving 2.5times higher throughput than baselines while preserving superior performance. Extensive experiments over flow-based policies π_0, π_{0.5}, and one-step regression policy π_{reg} demonstrate that MoH yields consistent and significant gains on both simulations and real-world tasks. Notably, under mixed-task setting, π_{0.5} with MoH reaches a new state-of-the-art with 99% average success rate on LIBERO after only 30k training iterations. Project page: https://github.com/Timsty1/MixtureOfHorizons
69268d88243b2216fb75caff
Dong Jing
claimed_verified
2025-12-02T08:49:57.902Z
642c25e68f90c557f7423eb0
/avatars/6f86a611994590b871e9ecfed61dc06f.svg
Dong Jing
Timsty
user
Timsty
69268d88243b2216fb75cb00
Gang Wang
69268d88243b2216fb75cb01
Jiaqi Liu
69268d88243b2216fb75cb02
Weiliang Tang
69268d88243b2216fb75cb03
Zelong Sun
69268d88243b2216fb75cb04
Yunchao Yao
69268d88243b2216fb75cb05
Zhenyu Wei
69268d88243b2216fb75cb06
Yunhui Liu
69268d88243b2216fb75cb07
Zhiwu Lu
69268d88243b2216fb75cb08
Mingyu Ding
Mixture of Horizons in Action Chunking
Vision-language-action (VLA) models have shown remarkable capabilities in robotic manipulation, but their performance is sensitive to the action chunk length used during training, termed 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 mixture of horizons (MoH) strategy. MoH rearranges the action chunk into several segments with different horizons, processes them in parallel with a shared action transformer, and fuses outputs with a light linear gate. It has three appealing benefits. 1) MoH exploits long-term foresight and short-term precision jointly within a single model, improving both performance and generalizability to complex tasks. 2) MoH is plug-and-play for full-attention action modules with minimal training or inference overhead. 3) MoH enables dynamic inference with adaptive horizons, which selects stable actions through cross-horizon consensus, achieving 2.5times higher throughput than baselines while preserving superior performance. Extensive experiments over flow-based policies π_0, π_{0.5}, and one-step regression policy π_{reg} demonstrate that MoH yields consistent and significant gains on both simulations and real-world tasks. Notably, under mixed-task setting, π_{0.5} with MoH reaches a new state-of-the-art with 99% average success rate on LIBERO after only 30k training iterations. Project page: https://github.com/Timsty1/MixtureOfHorizons
A mixture of horizons strategy in VLA models improves performance and generalizability by combining long-term foresight and short-term precision, achieving higher throughput and superior performance in robotic manipulation tasks.
Vision-language-action models action chunk length horizon mixture of horizons (MoH) action transformer cross-horizon consensus flow-based policies one-step regression policy LIBERO