Viewing direct policy optimization from a physical perspective
Introduction Direct Preference Optimization (DPO) is a method used to fine-tune large language models by directly optimizing their parameters based on human feedback. DPO effectively addresses the challenges associated with supervised fine-tuning during the advanced stages of reinforcement learning. The concept behind DPO is to integrate preference data directly into the model’s training process, adjusting the model’s parameters to increase the likelihood of generating outputs similar to those preferred by humans....