The article compares the learning efficiency of reinforcement learning (RL) and supervised learning, highlighting that RL requires significantly more computational effort to obtain meaningful feedback. It discusses how the quality of information per sample is generally lower in RL, especially early in training, leading to noisy gradient estimates and less efficient learning. The author emphasizes the importance of maintaining an optimal pass rate to improve RL performance.