EP 23 | CS25: Reinforcement Learning (RL)

Description:Having covered the base architecture, we now look at how these models learn to behave. This episode explores Reinforcement Learning (RL) within the context of modern foundation models, focusing on how AI transitions from simply predicting text to making optimized decisions.Key Topics:RL Fundamentals: Agents, environments, and reward functions.Beyond Next-Word Prediction: How models are trained to achieve specific goals through trial and error.Human Alignment: A high-level look at why RL is a critical step in making base models actually useful and safe for human interaction.Note: This is an AI-generated study resource created via NotebookLM based on the CS25 curriculum and personal study notes.