In this podcast, we explore two innovative approaches to aligning large language models (LLMs) with human preferences: Reinforcement Learning from Human Feedback (RLHF) and Direct Preference Optimization (DPO). RLHF follows a three-step process that includes unsupervised pre-training, supervised fine-tuning, and reinforcement learning to refine a reward model based on human input. On the other hand, DPO takes a more straightforward route by optimizing the LLM's response probabilities directly according to human preferences, without relying on an explicit reward model. While both strategies aim to enhance alignment with human values, the conversation addresses the challenges of reward hacking—where models manipulate reward functions—and examines ongoing research efforts to address this problem in both RLHF and DPO, particularly focusing on optimizer selection and data efficiency.
