arXiv cs.AI· Jack Lu, Yurong You, Boyi Li, Yulong Cao, Thomas Tian, Chaowei Xiao, Xiangyu Chen, Wenjie Luo, Boris Ivanovic, Marco Pavone, Yan Wang·· 3 天前AI 评分40
TacitVLA：无需推理延迟的推理型视觉-语言-动作模型
TacitVLA: Reasoning Vision-Language-Action Models Without Reasoning Latency
AI 导读
TacitVLA 是一种推理型 VLA 模型，其动作专家不会关注当前调用生成的推理文本，因为推理监督本身已改善骨干表示，从而无需等待自回归解码。在 RoboMME、RMBench、LIBERO 和 PhysicalAI-AV 四个基准上，它以最高 3.4 倍更低延迟匹配显式推理版本性能，并以最高 +17.1% 成功率匹配无推理架构的延迟。
正文 · 原文
Authors:Jack Lu, Yurong You, Boyi Li, Yulong Cao, Thomas Tian, Chaowei Xiao, Xiangyu Chen, Wenjie Luo, Boris Ivanovic, Marco Pavone, Yan Wang
Abstract:Textual reasoning before acting has improved vision-language-action (VLA) models, but at the cost of latency: every action conditioned on the reasoning text must wait for autoregressive decoding. We show that actions do not need to wait for or condition on the generated reasoning to benefit from it, because the reasoning supervision already improves the backbone representation. We introduce TacitVLA, a reasoning VLA whose action expert never attends to the reasoning it generates for the current call. On long-horizon tasks where the reasoning text also serves as episodic memory, it can generate each entry during action execution rather than before acting. We evaluate it in a controlled study on four benchmarks spanning manipulation and autonomous driving, memory and non-memory tasks: RoboMME, RMBench, LIBERO, and PhysicalAI-AV. On all four, TacitVLA matches the performance of its explicit-reasoning counterpart at up to 3.4x lower latency, and matches the latency of the same architecture trained without reasoning at up to +17.1% success. In absolute terms, it surpasses the best previously published results on the RoboMME Counting suite (82.7% vs. 66.8%) and RMBench (77.5% vs. 56.5%), and reaches 72.9% on LIBERO-Plus and a 52.5% scene score on the public split of the PhysicalAI-AV closed-loop driving challenge. Project page: this https URL.
| Comments: | Project page: this https URL |
| Subjects: | Artificial Intelligence (cs.AI); Robotics (cs.RO) |
| Cite as: | arXiv:2610.17540 [cs.AI] |
| (or arXiv:2610.17540v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2610.17540 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Jack Lu [view email]
[v1]
Tue, 13 Oct 2026 01:09:25 UTC (7,480 KB)
来源：arXiv cs.AI · arxiv.org