Nathan Lambert

This talk explores the evolution of AI reasoning capabilities, moving beyond benchmark scores to focus on practical applications and future development. It posits that while current models excel at specific skills like math and code, the next frontier lies in planning, strategy, and abstraction. Achieving this requires a shift in how models are trained, emphasizing human effort in developing new algorithmic methods and data acquisition strategies.