DPO vs PPO for LLMs: Key Differences & Use Cases

Large Language Models (LLMs) such as ChatGPT and Gemini require alignment to ensure their outputs align with human intentions, a process addressed by two main techniques: Proximal Policy Optimization (PPO) and Direct Preference Optimization (DPO). PPO, rooted in reinforcement learning, uses a reward model to optimize language models, making it effective for complex tasks like code generation but requiring extensive human feedback and computational resources. DPO, on the other hand, simplifies the process by directly adjusting model parameters based on human preferences without a reward model, making it more efficient and stable for tasks like dialogue and summarization. Clarifai's platform supports both methods, offering tools for data management, model training, and deployment, which helps streamline the alignment process. Emerging algorithms like ORPO and RLAIF aim to further refine preference optimization by reducing reliance on human annotation and increasing efficiency. The choice between DPO and PPO depends on task complexity, data availability, and computational resources, with hybrid strategies often providing balanced outcomes.