cs.AIFeb 24, 2026
SaveNoRD: A Data-Efficient Vision-Language-Action Model that Drives without Reasoning
Organizations: Applied Intuition · Texas A&M University · UC Berkeley
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 <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/
Figures & tables
Figure 1: Comparison of VLA training pipelines. (a) Existing approaches depend on large-scale reasoning data generation, followed by extensive SFT and RL fine-tuning. (b) In contrast, NoRD directly utilizes a small-scale driving dataset for SFT, and performs RL fine-tuning tailored for weak SFT policy, enabling data-efficient learning without reasoning supervision.
Figure 2: Reward distribution in the weak SFT model. The group-mean PDM score is shown with band representing the mean of the corresponding group standard deviation for NoRD-base . GRPO struggles to optimize high-variance regions (the majority) and is effective only in low-variance regions (the trajectories in green and red are for ground truth and NoRD-base prediction).
Figure 3: Evolution of group-mean PDM score during RL fine-tuning. (a) GRPO struggles to optimize samples with high group variance during training, particularly in the range – . (b) Dr. GRPO effectively optimizes high-variance samples during training, resulting in significant overall performance gains.
Figure 4: Qualitative comparison of RL fine-tuning (RLFT) on the weak SFT model using GRPO and Dr. GRPO. With Dr. GRPO, NoRD successfully learns complex maneuvers such as sharp turns and lane changes without collisions, whereas GRPO fails to optimize the weak SFT model ( NoRD-base ) and collides (in red).
| Model | PDMS |
| NoRD-base | 76.66 |
| NoRD-base + GRPO | 77.18 (+0.67%) |
| NoRD-base + Dr. GRPO | 85.62 (+11.68%) |
Table 1: Comparison of RL fine-tuning (RLFT) on NoRD-base with GRPO and Dr. GRPO on NAVSIM test set. While GRPO fails to improve NoRD-base , we get significant gains with Dr. GRPO.
Figure 5: Model architecture of NoRD . NoRD directly predicts action tokens without requiring reasoning traces, enabling a significantly more efficient training and inference pipeline.
| Model | w/o Reason | w/o Ensemble | RFS | ADE@3 |
| Poutine [ 36 ] | ✗ | ✓ | 7.986 | 1.2055 |
| HMVLM [ 40 ] | ✗ | ✓ | 7.736 | 1.3269 |
| DiffusionLTF | ✓ | ✗ | 7.717 | 1.3561 |
| UniPlan | ✓ | ✗ | 7.692 | 1.3083 |
| AutoVLA [ 51 ] | ✗ | ✓ | 7.556 | 1.3507 |
| NoRD | ✓ | ✓ | 7.709 | 1.2504 |
Table 2: Test results on the Waymo Vision-based End-to-End Driving Benchmark. NoRD achieves competitive performance, without reasoning or ensembling.
| Method | w/o R | w/o L | C | PDMS | Collision | DAC | Direction | Progress | TTC | Comfort |
| BEV-based Methods | ||||||||||
| UniAD [ 17 ] | ✓ | ✓ | 32 | 83.4 | 97.7 | 91.9 | - | 78.8 | 92.9 | 100 |
| Transfuser [ 7 ] | ✓ | ✗ | 3 | 84.0 | 97.7 | 92.8 | 97.9 | 79.2 | 92.8 | 100 |
| Hydra-MDP [ 24 ] | ✓ | ✗ | 3 | 86.5 | 98.2 | 96.2 | 95.8 | 78.7 | 94.6 | 100 |
| DiffusionDrive [ 25 ] | ✓ | ✗ | 3 | 88.1 | 98.2 | 96.2 | - | 82.2 | 94.7 | 88.1 |
| VLA-based Methods | ||||||||||
Table 3: Test results on NAVSIM benchmark (navtest subset). NoRD achieves competitive performance ( w/o R : Without reasoning data, w/o L : without LiDAR data, and C : Number of RGB frames; * BoN refers to the average over best score per sample out of 6 outputs with different random seeds).
Figure 6: Pareto-optimal curves on two driving benchmarks. (a) NoRD is the only VLA in NAVSIM operating in the high-performance, high–data-efficiency region using only RGB inputs. (b) NoRD achieves competitive RFS on WaymoE2E with a fraction of the training data, without ensembling or reasoning supervision. Shaded regions provide a qualitative categorization of model efficiency and performance for ease of visualization.
Figure 7: Qualitative Results on NAVSIM (navtest subset) . NoRD safely executes sharp turns, respects traffic lights, and avoids collisions, demonstrating robust driving behavior. The predicted trajectory is shown in red.
Figure 8: Qualitative Results on WaymoE2E test set . NoRD drives safely in challenging out-of-distribution scenarios like unsafe pedestrian crossing and construction site. The predicted trajectory is shown in red (stitched, center-cropped for visualization).
Figure 9: Comparison of token and runtime efficiency. NoRD is the most (a) token and (b) runtime efficient VLA.
Figure 10: Training improvement patterns for GRPO (top, red) and Dr. GRPO (bottom, blue) across intra-group variance levels. The line indicates no change in PDM score. GRPO shows strong improvements for low-variance samples with initial scores in (panel (a)), while Dr. GRPO outperforms GRPO for medium- and high-variance samples (panels (b) and (c)), with denser concentration above .
Figure 11: Training and validation curves for RL fine-tuning with GRPO and Dr.GRPO. Dr.GRPO (in red) consistently outperforms GRPO (in blue) on the (a) training and (b) validation sets by a significant margin.
| Method | PDMS | Collision | DAC | Direction | Progress | TTC | Comfort |
| NoRD-base | 76.66 | 96.45 | 86.37 | 94.62 | 71.58 | 90.37 | 99.97 |
| NoRD-base +GRPO | 77.18 | 91.89 | 90.12 | 91.84 | 80.06 | 80.13 | 99.96 |
| NoRD-base +Dr. GRPO | 85.62 | 97.56 | 94.92 | 95.94 | 79.30 | 93.53 | 100 |
Table 4: Detailed comparison of RL-fine-tuning of NoRD-base with GRPO and Dr. GRPO. Dr. GRPO based RL fine-tuning is almost always better than GRPO.
Figure 12: Example of NoRD inference. Given multi-view images, past trajectory, and the current velocity, acceleration, and driving command, NoRD directly predicts the trajectory tokens without explicit reasoning.
| Vocabulary Size | PDMS |
| 512 | 83.07 |
| 2048 | 85.62 |
Table 5: Effect of k-disc vocabulary size on the performance of NoRD on navtest.
| Metric Name | Value |
| Construction Score | 8.072616 |
| Intersection Score | 7.9252014 |
| Pedestrian Score | 7.7775736 |
| Cyclist Score | 7.8055406 |
| Multi Lane Maneuver Score | 7.8262477 |
| Single Lane Maneuver Score | 8.308635 |
Table 6: Detailed results on WaymoE2E Test Set.
Figure 13: Failure cases of NoRD . The predicted trajectory is shown in red and the violations marked in red circle.