Reasoning-VLA: A Fast and General Vision-Language-Action Reasoning Model for Autonomous Driving

• URL: http://arxiv.org/abs/2511.19912v1
• Date: Tue, 25 Nov 2025 04:40:11 GMT
• Title: Reasoning-VLA: A Fast and General Vision-Language-Action Reasoning Model for Autonomous Driving
• Authors: Dapeng Zhang, Zhenlong Yuan, Zhangquan Chen, Chih-Ting Liao, Yinda Chen, Fei Shen, Qingguo Zhou, Tat-Seng Chua,
• Abstract summary: Reasoning-VLA achieves state-of-the-art performance, superior generalization capability, and the excellent inference speed reported to date.<n>We consolidate eight publicly available autonomous driving datasets into a standardized, Chain-of-language reasoning-based, and easy-to-use data format for model training.
• Score: 46.99350914451702
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Vision-Language-Action (VLA) models have recently shown strong decision-making capabilities in autonomous driving. However, existing VLAs often struggle with achieving efficient inference and generalizing to novel autonomous vehicle configurations and driving scenarios. In this paper, we propose Reasoning-VLA, a general and fast action-generation VLA framework. The proposed model employs a set of learnable action queries, initialized via Gaussian sampling from ground-truth trajectories within the training corpus. These learnable queries interact with reasoning-enhanced vision-language features to generate continuous action trajectories in parallel. To promote robust generalization, we consolidate eight publicly available autonomous driving datasets into a standardized, Chain-of-Thought reasoning-based, and easy-to-use data format for model training. Leveraging both supervised learning and reinforcement learning fine-tuning, extensive empirical evaluations across multiple benchmarks demonstrate that Reasoning-VLA achieves state-of-the-art performance, superior generalization capability, and the excellent inference speed reported to date.

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