ExploreVLA: Dense World Modeling and Exploration for End-to-End Autonomous Driving

arXiv:2604.02714 · cs.CV · Submitted 2026-04-03 · Read on arXiv

cs.CV

Submitted: 2026-04-03

Updated: 2026-06-29

Project page: https://zihaosheng.github.io/ExploreVLA

Terminology

• World Model — 43× in this paper · explained in 7 episodes
• Reinforcement Learning — 14× in this paper · explained in 4 episodes
• GRPO — 13× in this paper · explained in 6 episodes
• Fine-Tuning — 12× in this paper · explained in 5 episodes
• Benchmark — 11× in this paper · explained in 2 episodes
• Transformer — 7× in this paper · explained in 2 episodes
• VLA Models — 6× in this paper · explained in 3 episodes
• International Conference on Computer Vision — 5× in this paper
• Ablation — 4× in this paper · explained in 1 episode
• Group Relative Policy Optimization — 4× in this paper
• Ablation study — 3× in this paper · explained in 1 episode
• Embeddings — 3× in this paper · explained in 2 episodes
• Fidelity — 3× in this paper · explained in 2 episodes
• Generalization — 3× in this paper · explained in 13 episodes
• Tokenization — 3× in this paper · explained in 2 episodes
• Unified Framework — 3× in this paper · explained in 5 episodes
• Adaptation — 2× in this paper · explained in 1 episode
• Attention Mechanism — 2× in this paper · explained in 3 episodes
• Core — 2× in this paper · explained in 2 episodes
• Group Relative Policy Optimization (GRPO) — 2× in this paper · explained in 5 episodes
• Policy World Model — 2× in this paper
• RAG — 2× in this paper · explained in 3 episodes
• Reinforcement Learning (RL) — 2× in this paper · explained in 25 episodes
• Sparsity — 2× in this paper · explained in 4 episodes
• 3D Gaussian Splatting — 1× in this paper · explained in 2 episodes
• Benchmarking — 1× in this paper · explained in 3 episodes
• Consistency — 1× in this paper · explained in 3 episodes
• Data Augmentation — 1× in this paper · explained in 7 episodes
• Diffusion Model — 1× in this paper · explained in 8 episodes
• Distillation — 1× in this paper · explained in 2 episodes
• Domain Adaptation — 1× in this paper · explained in 5 episodes
• Foundation Model — 1× in this paper · explained in 3 episodes
• Gaussian Splatting — 1× in this paper · explained in 4 episodes
• Generalizability — 1× in this paper · explained in 6 episodes
• Generative Model — 1× in this paper · explained in 2 episodes
• Mixture of Experts — 1× in this paper · explained in 2 episodes
• Multimodal Dataset — 1× in this paper · explained in 2 episodes
• Multimodal Learning — 1× in this paper · explained in 2 episodes
• Online Reinforcement Learning — 1× in this paper · explained in 2 episodes
• Regret — 1× in this paper · explained in 3 episodes
• Robustness — 1× in this paper · explained in 5 episodes
• Scaling Laws — 1× in this paper · explained in 4 episodes

Sources

• Pseudo-Simulation for Autonomous Driving
• Janus-Pro: Unified Multimodal Understanding and Generation with Data and Model Scaling
• A Survey of World Models for Autonomous Driving
• GAIA-1: A Generative World Model for Autonomous Driving
• EMMA: End-to-End Multimodal Model for Autonomous Driving
• DiffVLA: Vision-Language Guided Diffusion Planning for Autonomous Driving
• OmniNWM: Omniscient Driving Navigation World Models
• Hydra-MDP++: Advancing End-to-End Driving via Expert-Guided Hydra-Distillation
• DriveVLA-W0: World Models Amplify Data Scaling Law in Autonomous Driving
• ReCogDrive: A Reinforced Cognitive Framework for End-to-End Autonomous Driving
• Textbooks Are All You Need II: phi-1.5 technical report
• Hydra-MDP: End-to-end Multimodal Planning with Multi-target Hydra-Distillation
• UniWorld: Autonomous Driving Pre-training via World Models
• DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models
• Centaur: Robust End-to-End Autonomous Driving with Test-Time Training
• Alpamayo-R1: Bridging Reasoning and Action Prediction for Generalizable Autonomous Driving in the Long Tail
• Show-o: One Single Transformer to Unify Multimodal Understanding and Generation
• UniDrive-WM: Unified Understanding, Planning and Generation World Model for Autonomous Driving
• AD-R1: Closed-Loop Reinforcement Learning for End-to-End Autonomous Driving with Impartial World Models
• ReSim: Reliable World Simulation for Autonomous Driving

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