This comprehensive study examines Direct Preference Optimization (DPO) against Proximal Policy Optimization (PPO) in aligning LLMs with human preferences. It reveals essential factors for improved performance and demonstrates that PPO can outperform other methods on various RLHF benchmarks.

Key Highlights:

• Questions the current preference for DPO over PPO in LLM alignment.
• Delves into the algorithmic properties and limitations of DPO.
• Benchmarks DPO and PPO across numerous RLHF testbeds, showing PPO’s potential to achieve superior results.

The assessment presented in this paper challenges prevailing practices and suggests that more focus should be given to PPO in future LLM alignment efforts. The insights could facilitate better human-AI interaction experiences across a range of applications.

Read the full comparative study on LLM alignment.