Fast-ThinkAct: Efficient Vision-Language-Action Reasoning via Verbalizable Latent Planning
Chi-Pin Huang Yunze Man Zhiding Yu Min-Hung Chen Jan Kautz Yu-Chiang Frank Wang Fu-En Yang
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Abstract
Vision-Language-Action (VLA) tasks require reasoning over complex visual scenes and executing adaptive actions in dynamic environments. While recent studies on reasoning VLAs show that explicit chain-of-thought (CoT) can improve generalization, they suffer from high inference latency due to lengthy reasoning traces. We propose Fast-ThinkAct, an efficient reasoning framework that achieves compact yet performant planning through verbalizable latent reasoning. Fast-ThinkAct learns to reason efficiently with latent CoTs by distilling from a teacher, driven by a preference-guided objective to align manipulation trajectories that transfers both linguistic and visual planning capabilities for embodied control. This enables reasoning-enhanced policy learning that effectively connects compact reasoning to action execution. Extensive experiments across diverse embodied manipulation and reasoning benchmarks demonstrate that Fast-ThinkAct achieves strong performance with up to 89.3% reduced inference latency over state-of-the-art reasoning VLAs, while maintaining effective long-horizon planning, few-shot adaptation, and failure recovery.引用本文复制引用
Chi-Pin Huang,Yunze Man,Zhiding Yu,Min-Hung Chen,Jan Kautz,Yu-Chiang Frank Wang,Fu-En Yang.Fast-ThinkAct: Efficient Vision-Language-Action Reasoning via Verbalizable Latent Planning[EB/OL].(2026-02-24)[2026-04-03].https://arxiv.org/abs/2601.09708.学科分类
计算技术、计算机技术
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