Zhexi Lian, Haoran Wang, Xuerun Yan, Weimeng Lin, Xianhong Zhang, Yongyu Chen, Jia Hu

Researchers propose PaIR-Drive, a parallel training framework that combines imitation learning and reinforcement learning for end-to-end autonomous driving without sequential fine-tuning. Rather than using RL to refine a pretrained IL policy, the method trains both branches in parallel with separate objectives, eliminating policy drift and performance ceilings. The approach achieves competitive benchmarks on NAVSIMv1 and v2 while outperforming existing RL fine-tuning methods and even correcting suboptimal human driving behaviors.