Accelerated Preference Optimization for Large Language Model Alignment (2410.06293v1)

Abstract: Reinforcement Learning from Human Feedback (RLHF) has emerged as a pivotal tool for aligning LLMs with human preferences. Direct Preference Optimization (DPO), one of the most popular approaches, formulates RLHF as a policy optimization problem without explicitly estimating the reward function. It overcomes the stability and efficiency issues of two-step approaches, which typically involve first estimating the reward function and then optimizing the policy via proximal policy optimization (PPO). Since RLHF is essentially an optimization problem, and it is well-known that momentum techniques can accelerate optimization both theoretically and empirically, a natural question arises: Can RLHF be accelerated by momentum? This paper answers this question in the affirmative. In detail, we first show that the iterative preference optimization method can be viewed as a proximal point method. Based on this observation, we propose a general Accelerated Preference Optimization (APO) framework, which unifies many existing preference optimization algorithms and employs Nesterov's momentum technique to speed up the alignment of LLMs. Theoretically, we demonstrate that APO can achieve a faster convergence rate than the standard iterative preference optimization methods, including DPO and Self-Play Preference Optimization (SPPO). Empirically, we show the superiority of APO over DPO, iterative DPO, and other strong baselines for RLHF on the AlpacaEval 2.0 benchmark.

Accelerated Preference Optimization for LLM Alignment

The paper "Accelerated Preference Optimization for LLM Alignment" explores the integration of momentum techniques into Reinforcement Learning from Human Feedback (RLHF) to optimize LLMs. The authors propose the Accelerated Preference Optimization (APO) framework, which builds on Direct Preference Optimization (DPO) by incorporating Nesterov's momentum technique to enhance convergence rates.

Key Contributions

The primary contribution of this work is the formulation of a general APO framework that unifies existing preference optimization algorithms, such as DPO and Self-Play Preference Optimization (SPPO). The authors demonstrate both theoretically and empirically that APO achieves faster alignment of LLMs compared to standard iterative preference optimization methods.

Theoretical Insights

The paper rigorously proves that APO can outperform traditional approaches in terms of convergence speed. Theoretical analysis shows that the APO framework achieves a sub-optimality gap of $\tilde O\big((1-\alpha)/t\big)$, where $\alpha$ is the momentum parameter, compared to the $\tilde O(1/t)$ gap of iterative DPO. This advancement is facilitated by modeling the policy optimization process as a proximal point method, seamlessly integrating momentum to expedite convergence.

Empirical Outcomes

Empirical evaluations on the AlpacaEval 2.0 benchmark reveal APO's superior performance over traditional DPO and other baselines. Notably, APO demonstrates significant improvements in preference alignment with a higher length-controlled win rate, achieving a 31.73% rate, surpassing DPO. Further evaluations across multiple tasks from MT-Bench corroborate APO's efficacy, reinforcing its potential in real-world applications.

Implications and Future Directions

This research has profound implications for advancing LLM alignment with human preferences. The integration of momentum techniques in RLHF frameworks may lead to more efficient and effective LLMs, enhancing their applicability across diverse domains. Future work could extend APO to accommodate general preference models beyond the Bradley-Terry framework, and explore adaptive momentum strategies to further refine convergence rates.

Conclusion

The paper presents a compelling case for the use of accelerated optimization methods in the alignment of LLMs. By leveraging momentum techniques, the APO framework sets a new benchmark for efficiency in aligning LLMs with human feedback, offering a promising pathway for future exploration in AI alignment methodologies.