The paper introduces γ-PO, a dynamic target margin preference optimization algorithm that enhances Large Language Models’ alignment by adjusting reward margins at the pairwise level, leading to improved performance with minimal impact on training.
The alignment of Large Language Models (LLMs) is crucial for ensuring their
safety and reliability in practical applications. Direct Preference
Optimization (DPO) has emerged as an efficient method that directly optimizes
models using preference pairs, significantly reducing resource demands.
However, the effectiveness of DPO heavily depends on the data quality, which is
frequently compromised by noise. In this work, we propose gamma-PO, a
dynamic target margin preference optimization algorithm that adjust reward
margins at the pairwise level. By introducing instance-specific margin
calibration, gamma-PO strategically prioritizes high-confidence pairs (those
demonstrating higher reward margins) while suppressing potential noise from
ambiguous pairs. Moreover, gamma-PO is a plug-and-play method, compatible
with variants of DPO that rely on reward margin between preference pairs.
Across benchmarks such as AlpacaEval2 and Arena-Hard, gamma-PO achieves an
average 4.4\% improvement over other baselines, setting new benchmarks for
state-of-the-art performance. Additionally, gamma-PO requires minimal code
changes and has a negligible impact on training efficiency, making it a robust
solution for enhancing LLMs alignment. Our codes are available at
https://github.com/sunjie279/gammaPO{https://github.com/sunjie279/gammaPO}.
