Berkeley EECS
John Schulman - Reinforcement Learning from Human Feedback: Progress and Challenges
John Schulman, co-founder of OpenAI and lead of the ChatGPT RLHF work, gives the clearest available argument for why supervised fine tuning alone teaches a model to hallucinate and why reinforcement learning is the lever that can teach it to hedge instead. The core idea: imitation makes the model assert things it has no internal evidence for, while a reward that scores confident wrong answers below honest uncertainty makes calibration the optimal policy. He then walks through reward models, retrieval and citation as the route to verifiability, and the open problems he had not solved.