Reinforcement Learning from Humans

Lewis explains the innovative approach of reinforcement learning from human feedback (RLHF) in improving summarization models. By generating summaries and having humans rate them, a reward model is trained to distinguish quality outputs. This method has evolved to encompass a broader range of tasks, utilizing instruction data to enhance model performance while addressing potential output issues.