Fast-ThinkAct: 言語化可能な潜在プランニングによる効率的な視覚-言語-行動推論
Fast-ThinkAct: Efficient Vision-Language-Action Reasoning via Verbalizable Latent Planning
推論トレースを潜在的な思考連鎖に蒸留し、推論VLAの推論遅延を最大89.3%削減しつつ長期的プランニングや適応性能を維持する手法を提案。
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著者: Chi-Pin Huang, Yunze Man, Zhiding Yu, Min-Hung Chen, Jan Kautz, Yu-Chiang Frank Wang, Fu-En Yang
分類: cs.CV, cs.AI, cs.LG, cs.RO
原文アブストラクト
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.
