NoRD: A Data-Efficient Vision-Language-Action Model that Drives without Reasoning

arXiv (Cornell University) 2026 Preprint Open access

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Authors: Ishaan Singh Rawal, Shubh Gupta, Yihan Hu, Wei Zhan
DOI: 10.48550/arxiv.2602.21172
Cited by: 0

Abstract

Vision-Language-Action (VLA) models are advancing autonomous driving by replacing modular pipelines with unified end-to-end architectures. However, current VLAs face two expensive requirements: (1) massive dataset collection, and (2) dense reasoning annotations. In this work, we address both challenges with NORD (No Reasoning for Driving). Compared to existing VLAs, NORD achieves competitive performance while being fine-tuned on

Fields of Study & Subjects

Autonomous Vehicle Technology and Safety Reinforcement Learning in Robotics Multimodal Machine Learning Applications

Publication details

Journal / source
arXiv (Cornell University)
Publication date
24 February 2026
Language
EN
Document type
Preprint