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

arXiv: https://arxiv.org/abs/2602.21172 · HTML: https://mlsanity.com/paper/2602.21172/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- Placed in Chain-of-thought reasoning for driving, one of 17 directions in LLM reasoning.
- Builds on 21 of its 51 references that are in the map.
- First posted on arXiv 24 Feb 2026.

## Placed in

- LLM reasoning / [Chain-of-thought reasoning for driving](https://mlsanity.com/lr/chain-of-thought-reasoning-for-driving/)
- Vision-language-action models / [VLA for autonomous driving](https://mlsanity.com/vla/vla-for-autonomous-driving/)

## Builds on (most cited first, 21 of 21)

- [Qwen2.5-VL Technical Report](https://mlsanity.com/paper/2502.13923/) (2025)
- [HybridFlow: A Flexible and Efficient RLHF Framework](https://mlsanity.com/paper/2409.19256/) (2024)
- [Process Reinforcement through Implicit Rewards](https://mlsanity.com/paper/2502.01456/) (2025)
- [Hydra-MDP: End-to-end Multimodal Planning with Multi-target Hydra-Distillation](https://mlsanity.com/paper/2406.06978/) (2024)
- [ReCogDrive: A Reinforced Cognitive Framework for End-to-End Autonomous Driving](https://mlsanity.com/paper/2506.08052/) (2025)
- [GPG: A Simple and Strong Reinforcement Learning Baseline for Model Reasoning](https://mlsanity.com/paper/2504.02546/) (2025)
- [Alpamayo-R1: Bridging Reasoning and Action Prediction for Generalizable Autonomous Driving in the Long Tail](https://mlsanity.com/paper/2511.00088/) (2025)
- [EgoVLA: Learning Vision-Language-Action Models from Egocentric Human Videos](https://mlsanity.com/paper/2507.12440/) (2025)
- [GraspVLA: a Grasping Foundation Model Pre-trained on Billion-scale Synthetic Action Data](https://mlsanity.com/paper/2505.03233/) (2025)
- [Curriculum Reinforcement Learning from Easy to Hard Tasks Improves LLM Reasoning](https://mlsanity.com/paper/2506.06632/) (2025)
- [DiffVLA: Vision-Language Guided Diffusion Planning for Autonomous Driving](https://mlsanity.com/paper/2505.19381/) (2025)
- [Impromptu VLA: Open Weights and Open Data for Driving Vision-Language-Action Models](https://mlsanity.com/paper/2505.23757/) (2025)
- [AdaThinkDrive: Adaptive Thinking via Reinforcement Learning for Autonomous Driving](https://mlsanity.com/paper/2509.13769/) (2025)
- [AgentThink: A Unified Framework for Tool-Augmented Chain-of-Thought Reasoning in Vision-Language Models for Autonomous Driving](https://mlsanity.com/paper/2505.15298/) (2025)
- [AutoDrive-R2: Incentivizing Reasoning and Self-Reflection Capacity for VLA Model in Autonomous Driving](https://mlsanity.com/paper/2509.01944/) (2025)
- [Diffusion Trajectory-guided Policy for Long-horizon Robot Manipulation](https://mlsanity.com/paper/2502.10040/) (2025)
- [HMVLM: Multistage Reasoning-Enhanced Vision-Language Model for Long-Tailed Driving Scenarios](https://mlsanity.com/paper/2506.05883/) (2025)
- [DriveAgent-R1: Advancing VLM-based Autonomous Driving with Active Perception and Hybrid Thinking](https://mlsanity.com/paper/2507.20879/) (2025)
- [Poutine: Vision-Language-Trajectory Pre-Training and Reinforcement Learning Post-Training Enable Robust End-to-End Autonomous Driving](https://mlsanity.com/paper/2506.11234/) (2025)
- [DARO: Difficulty-Aware Reweighting Policy Optimization](https://mlsanity.com/paper/2510.09001/) (2025)
- [More Than Meets the Eye? Uncovering the Reasoning-Planning Disconnect in Training Vision-Language Driving Models](https://mlsanity.com/paper/2510.04532/) (2025)

## Built on by (most cited first, 15 of 15)

- [UniDriveVLA: Unifying Understanding, Perception, and Action Planning for Autonomous Driving](https://mlsanity.com/paper/2604.02190/) (2026)
- [SpanVLA: Efficient Action Bridging and Learning from Negative-Recovery Samples for Vision-Language-Action Model](https://mlsanity.com/paper/2604.19710/) (2026)
- [SimWAM: A Simple World Action Model for End-to-End Autonomous Driving](https://mlsanity.com/paper/2608.07468/) (2026)
- [Qwen-Drive-1.0: An Initial Step towards a Vision-Language Foundation Model for Autonomous Driving](https://mlsanity.com/paper/2609.00111/) (2026)
- [DriveMA: Driving Vision-Language-Action Models with verifiable Meta-Actions](https://mlsanity.com/paper/2605.31271/) (2026)
- [IDOL: Inverse-Dynamics-Guided Future Prediction for End-to-End Autonomous Driving](https://mlsanity.com/paper/2605.31476/) (2026)
- [Post-Training in End-to-End Autonomous Driving](https://mlsanity.com/paper/2607.08072/) (2026)
- [Driving Intents Amplify Planning-Oriented Reinforcement Learning](https://mlsanity.com/paper/2605.12625/) (2026)
- [DriveMA: Rethinking Language Interfaces in Driving VLAs with One-Step Meta-Actions](https://mlsanity.com/paper/2605.21273/) (2026)
- [D3-MoE:Dual Disentangled Diffusion Mixture-of-Experts for Style-Controllable End-to-End Autonomous Driving](https://mlsanity.com/paper/2606.04884/) (2026)
- [Latent-Centroid Steering: Single-Pass Classifier-Free Guidance for Command-Aligned Autonomous Driving](https://mlsanity.com/paper/2608.00237/) (2026)
- [SPVC: Structured and Panoptic Video Fixing for Cross-Dataset Driving Scene Rendering](https://mlsanity.com/paper/2608.17420/) (2026)
- [RAF-VLA: Representation Alignment with the Future for End-to-End Autonomous Driving](https://mlsanity.com/paper/2609.17728/) (2026)
- [CAR-VLA: Complexity-Aware and Risk-Adaptive Reasoning for Autonomous Driving](https://mlsanity.com/paper/2609.34387/) (2026)
- [When the Score Becomes the Target: Rethinking Metric Validity in Autonomous Driving](https://mlsanity.com/paper/2609.34440/) (2026)

## Abstract (arXiv)

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 <60% of the data and no reasoning annotations, resulting in 3x fewer tokens. We identify that standard Group Relative Policy Optimization (GRPO) fails to yield significant improvements when applied to policies trained on such small, reasoning-free datasets. We show that this limitation stems from difficulty bias, which disproportionately penalizes reward signals from scenarios that produce high-variance rollouts within GRPO. NORD overcomes this by incorporating Dr. GRPO, a recent algorithm designed to mitigate difficulty bias in LLMs. As a result, NORD achieves competitive performance on Waymo and NAVSIM with a fraction of the training data and no reasoning overhead, enabling more efficient autonomous systems. Website: https://nord-vla-ai.github.io/