{"ID":2836727,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.19912","arxiv_id":"2511.19912","title":"Reasoning-VLA: A Fast and General Vision-Language-Action Reasoning Model for Autonomous Driving","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.","short_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 genera...","url_abs":"https://arxiv.org/abs/2511.19912","url_pdf":"https://arxiv.org/pdf/2511.19912v1","authors":"[\"Dapeng Zhang\",\"Zhenlong Yuan\",\"Zhangquan Chen\",\"Chih-Ting Liao\",\"Yinda Chen\",\"Fei Shen\",\"Qingguo Zhou\",\"Tat-Seng Chua\"]","published":"2025-11-25T04:40:11Z","proceeding":"cs.CV","tasks":"[\"cs.CV\",\"cs.RO\"]","methods":"[\"Reinforcement Learning\"]","has_code":false}