Direct Preference Optimization (DPO): A low cost alternative to train LLM models

Building the best Large Language Models (LLMs) like ChatGPT is expensive and inaccessible for most researchers. Reinforcement Learning from Human Feedback (RLHF), a method used to optimize models, is costly and requires extensive resources. However, Direct Preference Optimization (DPO) is a mathematical breakthrough that aligns the trained model with human preferences without the need for a reinforcement learning loop. DPO uses algebra to define the reward and train the LLM directly, eliminating the need for a separate reward model. This allows for a more efficient and cost-effective way to optimize language models.