Fast-ThinkAct: 通过可言说的潜在规划实现高效的视觉-语言-动作推理
Fast-ThinkAct: Efficient Vision-Language-Action Reasoning via Verbalizable Latent Planning
AI总结：
Fast-ThinkAct通过可言说的潜在规划实现高效的视觉-语言-动作推理，显著降低推理延迟并保持长周期规划能力。
摘要：
视觉-语言-动作（VLA）任务需要对复杂的视觉场景进行推理，并在动态环境中执行适应性动作。尽管最近关于推理VLA的研究表明，显式的思路链（CoT）可以提高泛化能力，但其由于推理轨迹过长而面临较高的推理延迟。我们提出了Fast-ThinkAct，一种高效的推理框架，通过可言说的潜在推理实现紧凑而高效的规划。Fast-ThinkAct通过从教师模型中学习，利用偏好引导的目标对操纵轨迹进行对齐，从而转移语言和视觉规划能力，以实现基于推理的策略学习。这使紧凑的推理能够有效连接到动作执行。在多样化的具身操作和推理基准测试中，广泛实验表明，Fast-ThinkAct在保持有效长周期规划、少样本适应和故障恢复的同时，比最先进的推理VLA实现了高达89.3%的推理延迟降低。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
