Aakanksha Chowdhery

This talk explores the progression of large language models (LLMs) from early scaling laws to the current frontier of autonomous coding. It highlights how techniques like chain-of-thought prompting and reinforcement learning with human feedback have improved LLM capabilities. The core thesis is that reinforcement learning, particularly in domains with automated verification like coding, represents the next significant step in scaling LLM performance and building more intelligent systems.