Direct Preference Optimization (DPO) is a simplified and efficient approach to fine-tuning large language models (LLMs). It works by directly optimizing the model’s responses to human preferences, eliminating the need for a reward model and extensive sampling.

• DPO is based on the idea that the model can learn to optimize its responses to human preferences by directly comparing its outputs to the preferred responses.
• DPO uses a binary cross-entropy objective to measure the difference between the model’s outputs and the preferred responses.
• DPO can be used to fine-tune LLMs for a variety of tasks, including question answering, summarization, and chatbots

• DPO is an alternative to Reinforcement Learning from Human Feedback (RLHF), which was previously the most common method for fine-tuning LLMs.
• DPO is simpler, more efficient, and more stable than RLHF.
• DPO is a two-stage process: supervised fine-tuning (SFT) and preference learning.
• SFT is the first step, where the model is fine-tuned on a dataset of interest.
• Preference learning is the second step, where the model is fine-tuned using preference data, which is a set of human-labeled examples of preferred and rejected responses.
• DPO is implemented using the DPOTrainer library from the TRL (Transformer Reinforcement Learning) library.

• [2305.18290] Direct Preference Optimization: Your Language Model is Secretly a Reward Model (arxiv.org)
• Direct Preference Optimization (DPO): A Simplified Approach to Fine-tuning Large Language Models (plainenglish.io)