Direct Preference Optimization Beyond Chatbots

This post introduces Direct Preference Optimization (DPO) as a method to align language models with human preferences, extending beyond chatbot applications to general-purpose models. DPO offers a simpler and more stable alternative to Reinforcement Learning from Human Feedback (RLHF) by directly optimizing a policy against a reference policy using a binary classification loss on preference pairs. The post details the mathematical formulation of DPO and provides an example of its application to a non-chatbot model, demonstrating its versatility.