Fine-tuning is Not Enough: A Parallel Framework for Collaborative Imitation and Reinforcement Learning in End-to-end Autonomous Driving

ArXi:2603.13842v1 Announce Type: cross End-to-end autonomous driving is typically built upon imitation learning (IL), yet its performance is constrained by the quality of human nstrations. To overcome this limitation, recent methods incorporate reinforcement learning (RL) through sequential fine-tuning. However, such a paradigm remains suboptimal: sequential RL fine-tuning can