Accelerated Preference Optimization for Large Language Model Alignment
Jiafan He, Huizhuo Yuan, Quanquan Gu
TL;DR
This paper introduces an accelerated preference optimization framework for large language model alignment, employing Nesterov's momentum to improve convergence speed and outperform existing methods both theoretically and empirically.
Contribution
The paper proposes a unified accelerated preference optimization framework that leverages momentum techniques, significantly enhancing the efficiency of RLHF for LLM alignment.
Findings
APO achieves faster convergence than standard methods.
APO outperforms DPO and SPPO on AlpacaEval 2.0.
Theoretical analysis confirms improved convergence rates.
Abstract
Reinforcement Learning from Human Feedback (RLHF) has emerged as a pivotal tool for aligning large language models (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…
Peer Reviews
Decision·Submitted to ICLR 2025
1. This paper is well-written and easy to follow. 2. It provides a new theory for validating Nesterov's extrapolation idea in the iterative DPO framework.
1. There is a trade-off between optimization error and statistical error in the developed theory. Therefore, it is unclear whether Nesterov's acceleration offers a true advantage over the naive method. 2. Empirically and theoretically, the advantages and weaknesses compared to direct reward optimization algorithms (e.g., PPO) are unclear. 3. Experiment results are somewhat weak and does not validate the theory.
1. To the best of my knowledge, this is the first work that examines the converge of iterative DPO and proposes improvements towards the convergence specifically 2. The theoretical analysis demonstrates that APO does lead to faster convergence in comparison to standard DPO 3. The empirical results shows that APO leads to better alignment in comparison to the baselines.
1. To the best of my understanding, Equation 3.6 in the APO algorithm does not update the neural network weights. Instead it is derived by keeping reference of the policy from the previous and the current iterations. It is not clear how the final policy in step 7 is used in the next iterations in steps 5 and 6. 2. This work might benefit from additional baselines in the empirical studies - IPO, SPPO, SimPo, KTO and especially ExPO 3. To the best of my understanding, the extrapolation step adds a
1. The paper establishes a novel and intriguing connection between iterative preference optimization and proximal point optimization. This connection has the potential to inspire further research that leverages advanced techniques from classical optimization literature for preference optimization. 2. The paper presents a rigorous theoretical analysis of the proposed APO method, demonstrating that APO can achieve a faster convergence rate.
1. The theoretical results in this paper are not particularly strong and do not conclusively show that APO is superior to previous methods. Specifically, in Theorem 4.4, compared to DPO ($\alpha=0$), APO achieves a smaller optimization error by a factor of $1-\alpha$, but incurs a larger statistical error by a factor of $1/(1-\alpha)$. Therefore, in the finite-data case, it remains unclear whether APO is theoretically better than DPO. Similar concerns also arise in Theorem 4.8. 2. There is a mis
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Taxonomy
TopicsNatural Language Processing Techniques · Topic Modeling · Speech Recognition and Synthesis
MethodsDirect Preference Optimization · SPEED: Separable Pyramidal Pooling EncodEr-Decoder for Real-Time Monocular Depth Estimation on Low-Resource Settings