Length-Controlled Margin-Based Preference Optimization without Reference Model

TL;DR

This paper introduces LMPO, a novel preference optimization method that controls response length and reduces probability degradation in large language models, outperforming existing techniques in benchmark tests.

Contribution

LMPO presents a length-controlled margin-based loss with a uniform reference model, improving preference optimization stability and efficiency without relying on a reference model.

Findings

LMPO effectively controls response length in large language models.

It reduces probability degradation compared to existing methods.

LMPO outperforms state-of-the-art preference optimization techniques on multiple benchmarks.

Abstract

Direct Preference Optimization (DPO) is a widely adopted offline algorithm for preference-based reinforcement learning from human feedback (RLHF), designed to improve training simplicity and stability by redefining reward functions. However, DPO is hindered by several limitations, including length bias, memory inefficiency, and probability degradation. To address these challenges, we propose Length-Controlled Margin-Based Preference Optimization (LMPO), a more efficient and robust alternative. LMPO introduces a uniform reference model as an upper bound for the DPO loss, enabling a more accurate approximation of the original optimization objective. Additionally, an average log-probability optimization strategy is employed to minimize discrepancies between training and inference phases. A key innovation of LMPO lies in its Length-Controlled Margin-Based loss function, integrated within the Bradley-Terry framework. This loss function regulates response length while simultaneously widening the margin between preferred and rejected outputs. By doing so, it mitigates probability degradation for both accepted and discarded responses, addressing a significant limitation of existing methods. We evaluate LMPO against state-of-the-art preference optimization techniques on two open-ended large language models, Mistral and LLaMA3, across six conditional benchmarks. Our experimental results demonstrate that LMPO effectively controls response length, reduces probability degradation, and outperforms existing approaches. The code is available at https://github.com/gengxuli/LMPO.