Nathan Lambert releases lecture on RLHF and post-training regularization

This lecture explores how KL divergence prevents reward model over-optimization and explains why reinforcement learning generalizes better than supervised fine-tuning. It is the tenth installment in a series covering RLHF and post-training techniques.