Recent advancements in deep-learning-based driving planners have primarily focused on elaborate network engineering, yielding limited improvements. This paper diverges from conventional approaches by exploring three fundamental yet underinvestigated aspects: training policy, data efficiency, and evaluation robustness. We introduce EasyChauffeur, a reproducible and effective planner for both imitation learning (IL) and reinforcement learning (RL) on Waymax, a GPU-accelerated simulator. Notably, our findings indicate that the incorporation of on-policy RL significantly boosts performance and data efficiency. To further enhance this efficiency, we propose SNE-Sampling, a novel method that selectively samples data from the encoder's latent space, substantially improving EasyChauffeur's performance with RL. Additionally, we identify a deficiency in current evaluation methods, which fail to accurately assess the robustness of different planners due to significant performance drops from minor changes in the ego vehicle's initial state. In response, we propose Ego-Shifting, a new evaluation setting for assessing planners' robustness. Our findings advocate for a shift from a primary focus on network architectures to adopting a holistic approach encompassing training strategies, data efficiency, and robust evaluation methods.