Sometimes You Gotta Run Before You Can Walk: Run-then-Walk Scheduling Strategy for VLM Autonomous Driving

arXiv:2609.25831 · cs.RO, cs.CV · Submitted 2026-09-22 · Read on arXiv

cs.RO, cs.CV

Submitted: 2026-09-22

Updated: 2026-10-07

Terminology

• GRPO — 106× in this paper · explained in 6 episodes
• Benchmark — 13× in this paper · explained in 2 episodes
• Reinforcement Learning — 4× in this paper · explained in 4 episodes
• World Model — 4× in this paper · explained in 7 episodes
• Decode — 3× in this paper · explained in 2 episodes
• Fine-Tuning — 3× in this paper · explained in 5 episodes
• Vision-Language Model — 3× in this paper
• Consistency — 2× in this paper · explained in 3 episodes
• GRPO Objective — 2× in this paper · explained in 2 episodes
• Group Relative Policy Optimization — 2× in this paper
• Robustness — 2× in this paper · explained in 5 episodes
• Transformer — 2× in this paper · explained in 2 episodes
• 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
• Calibration — 1× in this paper · explained in 7 episodes
• Diffusion Model — 1× in this paper · explained in 8 episodes
• Flow Matching — 1× in this paper · explained in 8 episodes
• Group Relative Policy Optimization (GRPO) — 1× in this paper · explained in 5 episodes
• Latent World Models — 1× in this paper · explained in 2 episodes
• Multimodal Dataset — 1× in this paper · explained in 2 episodes
• Predictive Driver Model Score — 1× in this paper
• Reward Function — 1× in this paper · explained in 2 episodes
• Vision-Language Model (VLM) — 1× in this paper · explained in 3 episodes

Sources

• Pseudo-Simulation for Autonomous Driving
• DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning
• DriveFuture: Future-Aware Latent World Models for Autonomous Driving
• Vision-R1: Incentivizing Reasoning Capability in Multimodal Large Language Models
• EMMA: End-to-End Multimodal Model for Autonomous Driving
• AlphaDrive: Unleashing the Power of VLMs in Autonomous Driving via Reinforcement Learning and Reasoning
• Enhancing End-to-End Autonomous Driving with Latent World Model
• DriveVLA-W0: World Models Amplify Data Scaling Law in Autonomous Driving
• End-to-End Driving with Online Trajectory Evaluation via BEV World Model
• ReCogDrive: A Reinforced Cognitive Framework for End-to-End Autonomous Driving
• Hydra-MDP: End-to-end Multimodal Planning with Multi-target Hydra-Distillation
• DriveWorld-VLA: Unified Latent-Space World Modeling with Vision-Language-Action for Autonomous Driving
• GPT-Driver: Learning to Drive with GPT
• DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models
• DeepSeekMath-V2: Towards Self-Verifiable Mathematical Reasoning
• InsightDrive: Insight Scene Representation for End-to-End Autonomous Driving
• DriveVLM: The Convergence of Autonomous Driving and Large Vision-Language Models
• Latent-WAM: Latent World Action Modeling for End-to-End Autonomous Driving
• VLM-AD: End-to-End Autonomous Driving through Vision-Language Model Supervision
• AutoDrive-R$^2$: Incentivizing Reasoning and Self-Reflection Capacity for VLA Model in Autonomous Driving

Related papers

• FMT x: An Efficient and Asymptotically Optimal Extension of the Fast Marching Tree for Dynamic Replanning
• MPCFormer: A physics-informed data-driven approach for explainable socially-aware autonomous driving
• RoboLab: A High-Fidelity Simulation Benchmark for Analysis of Task Generalist Policies
• HRDexDB: A 4D Dexterous Grasping Dataset Across Human and Multiple Robot Embodiments
• APT: Action Expert Pretraining Improves Instruction Generalization of Vision-Language-Action Policies
• Fine-tuning is Not Enough: A Parallel Framework for Collaborative Imitation and Reinforcement Learning in End-to-end Autonomous Driving