AI RESEARCH
ExploreVLA: Dense World Modeling and Exploration for End-to-End Autonomous Driving
arXiv CS.CV
•
ArXi:2604.02714v1 Announce Type: new End-to-end autonomous driving models based on Vision-Language-Action (VLA) architectures have shown promising results by learning driving policies through behavior cloning on expert nstrations. However, imitation learning inherently limits the model to replicating observed behaviors without exploring diverse driving strategies, leaving it brittle in novel or out-of-distribution scenarios. Reinforcement learning (RL) offers a natural remedy by enabling policy exploration beyond the expert distribution.