The paper presents an analysis to determine if Direct Preference Optimization (DPO) outperforms Proximal Policy Optimization (PPO) for aligning LLMs with human preferences. Key findings include:

• Theoretical and practical evaluation showing potential limitations of DPO.
• Identifying key factors for optimizing PPO’s performance.
• Demonstrating PPO’s superiority across various RLHF testbeds, including dialogue and code generation.

Understanding the alignment methods is crucial for developing AI models that can safely interact with humans and generate high-quality responses. The comparison aids in improving the effectiveness of LLMs and indicates that PPO might be a better approach for real-world applications. Learn More