Pieter discusses how multitask learning can enhance network performance by allowing models to generalize better across various domains. He highlights the advantage of incorporating unsupervised tasks, which require no annotations or rewards, making it a cost-effective approach to multitasking. The conversation delves into the relationship between unsupervised tasks and core reinforcement learning processes, exploring their potential impact on performance.
