新的剪枝技术提高了World Action Models的效率

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-16 04:00

研究人员开发了CF-Prune，一个新颖的框架，旨在通过剪枝不必要的未来帧token来提高World Action Models (WAMs) 的效率。该方法无需重新训练，利用指令和动作注意力在初始去噪阶段识别和移除与任务无关的视觉数据。在各种机器人操作任务上的实验表明，与基线模型相比，CF-Prune可以实现高达1.33倍的显著推理加速，同时保持甚至提高任务成功率。 AI

影响该技术通过减少计算负载，有望实现更快、更高效的机器人系统。

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arXiv cs.CV TIER_1 English(EN) · Baekseung Kim, Junhoo Lee, Sohyeon Hong, Suhyun Jeon, Minkyu Kim, Seungyeon Kim, Donghoon Han, Nojun Kwak · 2026-10-16 04:00

面向世界动作模型的互补未来令牌剪枝

arXiv:2610.19957v1 Announce Type: cross Abstract: World action models (WAMs) jointly predict future visual states and robot actions, but processing dense future-frame tokens incurs substantial inference latency. We propose CF-Prune, a training-free framework for pruning future-fr…

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面向世界动作模型的互补未来令牌剪枝

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