This paper introduces Direct Preference Optimization (DPO) for fine-tuning LLMs on preference data. DPO simplifies the training process by directly optimizing a policy against a fixed reference policy, eliminating the need for a separate reward model. This approach offers a more stable and efficient method for aligning LLMs with human preferences, extending beyond conversational agents to other decision-making tasks.
Opening Kapyn…