Fine-tuning is Not Enough: A Parallel Framework for Collaborative Imitation and Reinforcement Learning in End-to-end Autonomous Driving
The discussion centered on PaIR-Drive, a parallel framework for autonomous driving that moves beyond sequential fine-tuning by splitting learning into two branches: Imitation Learning (IL) and Reinforcement Learning (RL). This collaborative system allows the AI to learn reliable human behavior while actively correcting suboptimal flaws in real-world data, resulting in enhanced safety and performance.