RefineDrive: Reliable Failure-Guided Learning for Vision-Language-Action Driving

arXiv:2609.35078 · cs.CV · Submitted 2026-09-28 · Read on arXiv

cs.CV

Submitted: 2026-09-28

Updated: 2026-09-28

Terminology

• GRPO — 11× in this paper · explained in 6 episodes
• Reinforcement Learning — 8× in this paper · explained in 4 episodes
• Ablation — 7× in this paper · explained in 1 episode
• Computer Vision and Pattern Recognition — 4× in this paper
• Benchmark — 2× in this paper · explained in 2 episodes
• Drivable Area Compliance — 2× in this paper
• Ego Progress — 2× in this paper
• Fine-Tuning — 2× in this paper · explained in 5 episodes
• No At-Fault Collision — 2× in this paper
• Vision-Language-Action (VLA) Models — 2× in this paper · explained in 2 episodes
• Ablation Studies — 1× in this paper · explained in 1 episode
• Adaptive Reasoning — 1× in this paper · explained in 2 episodes
• Aggregation — 1× in this paper · explained in 1 episode
• Alignment — 1× in this paper · explained in 1 episode
• Benchmarking — 1× in this paper · explained in 3 episodes
• Clustering — 1× in this paper · explained in 2 episodes
• Consistency — 1× in this paper · explained in 3 episodes
• Diffusion Model — 1× in this paper · explained in 8 episodes
• Distillation — 1× in this paper · explained in 2 episodes
• Failure Modes — 1× in this paper · explained in 3 episodes
• Foundation Model — 1× in this paper · explained in 3 episodes
• GRPO Objective — 1× in this paper · explained in 2 episodes
• Grounding — 1× in this paper · explained in 3 episodes
• Multimodal Reasoning — 1× in this paper · explained in 5 episodes
• Predictive Driver Model Score — 1× in this paper
• Self-Distillation — 1× in this paper · explained in 7 episodes
• Transformer — 1× in this paper · explained in 2 episodes
• VLA Models — 1× in this paper · explained in 3 episodes
• Verification — 1× in this paper · explained in 2 episodes
• Vision-Language Models (VLMs) — 1× in this paper · explained in 6 episodes
• Vision-language models — 1× in this paper · explained in 2 episodes
• World Model — 1× in this paper · explained in 7 episodes

Sources

• GPT-Driver: Learning to Drive with GPT
• DriveVLM: The Convergence of Autonomous Driving and Large Vision-Language Models
• ChauffeurNet: Learning to Drive by Imitating the Best and Synthesizing the Worst
• SafeAlign-VLA: A Negative-Enhanced Safe Alignment Framework for Risk-Aware Autonomous Driving
• FIRE-VLA: Failure-Informed Self-Evolution for Vision-Language-Action Models in Autonomous Driving
• Learning from Mistakes: Rollout-Retrieval Lifelong Policy Learning for Autonomous Driving
• DriveMA: Rethinking Language Interfaces in Driving VLAs with One-Step Meta-Actions
• DriveFine: Refining-Augmented Masked Diffusion VLA for Precise and Robust Driving
• DiffusionDriveV2: Reinforcement Learning-Constrained Truncated Diffusion Modeling in End-to-End Autonomous Driving
• ExploreVLA: Dense World Modeling and Exploration for End-to-End Autonomous Driving

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