The field of end-to-end autonomous driving is witnessing significant advancements with the integration of vision-language models, reinforcement learning, and diffusion-based planning. Researchers are focusing on developing systems that can handle long-tailed driving scenarios, rare events, and complex vehicle dynamics. The introduction of novel frameworks and pipelines, such as those that refine hard domains while preserving generalizable knowledge, is leading to improved performance, safety, and robustness in autonomous driving systems. Noteworthy papers in this area include ReCogDrive, which proposes a reinforced cognitive framework for end-to-end autonomous driving, and RoCA, which enables robust cross-domain autonomous driving. Reinforced Refinement with Self-Aware Expansion is also a notable work, as it presents a novel learning pipeline that refines hard domains while keeping a generalizable driving policy.