MPPO: Multi Pair-wise Preference Optimization for LLMs with Arbitrary Negative Samples

TL;DR

The paper introduces MPPO, a novel preference optimization algorithm for LLMs that efficiently utilizes preference data and outperforms existing methods across multiple benchmarks, especially in multi-reply scenarios.

Contribution

MPPO is the first to optimize preferences with arbitrary negative samples and multiple replies, significantly improving response quality and data efficiency.

Findings

MPPO outperforms DPO, ORPO, and SimPO on MT-Bench.

MPPO surpasses DPO and ORPO on Arena-Hard.

Pair-wise implementation yields the best performance.

Abstract

Aligning Large Language Models (LLMs) with human feedback is crucial for their development. Existing preference optimization methods such as DPO and KTO, while improved based on Reinforcement Learning from Human Feedback (RLHF), are inherently derived from PPO, requiring a reference model that adds GPU memory resources and relies heavily on abundant preference data. Meanwhile, current preference optimization research mainly targets single-question scenarios with two replies, neglecting optimization with multiple replies, which leads to a waste of data in the application. This study introduces the MPPO algorithm, which leverages the average likelihood of model responses to fit the reward function and maximizes the utilization of preference data. Through a comparison of Point-wise, Pair-wise, and List-wise implementations, we found that the Pair-wise approach achieves the best performance, significantly enhancing the quality of model responses. Experimental results demonstrate MPPO's outstanding performance across various benchmarks. On MT-Bench, MPPO outperforms DPO, ORPO, and SimPO. Notably, on Arena-Hard, MPPO surpasses DPO and ORPO by substantial margins. These achievements underscore the remarkable advantages of MPPO in preference optimization tasks.