In this podcast episode, the discussion explores how AI agents have evolved from specialized systems like AlphaGo and AlphaStar to versatile multimodal models such as Gemini. The focus is on a two-phase training approach consisting of imitation learning (the initial pre-training phase) and reinforcement learning (the subsequent post-training phase). Although scaling these models has led to impressive advancements, we are starting to see diminishing returns, highlighting the need for innovative architectural and algorithmic improvements. Looking ahead, the conversation emphasizes the importance of combining these foundational models with "digital bodies" that can utilize tools like search engines and coding capabilities. This integration aims to enhance their ability to make autonomous decisions and exhibit agent-like behavior, potentially paving the way towards Artificial General Intelligence (AGI). However, significant challenges persist, particularly in defining and establishing reliable reward signals for the post-training phase.
