CAPO: Confidence Aware Preference Optimization Learning for Multilingual Preferences

• URL: http://arxiv.org/abs/2511.07691v1
• Date: Wed, 12 Nov 2025 01:11:54 GMT
• Title: CAPO: Confidence Aware Preference Optimization Learning for Multilingual Preferences
• Authors: Rhitabrat Pokharel, Yufei Tao, Ameeta Agrawal,
• Abstract summary: Preference optimization is used to align large language models with human preferences, typically by fine-tuning on ranked response pairs.<n>We propose Confidence-Aware Preference Optimization (CAPO), which replaces DPO's fixed treatment of preference pairs with a dynamic loss scaling mechanism.<n>CAPO enhances robustness to noisy or low-margin comparisons, typically encountered in multilingual text.
• Score: 4.460583138505673
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Preference optimization is a critical post-training technique used to align large language models (LLMs) with human preferences, typically by fine-tuning on ranked response pairs. While methods like Direct Preference Optimization (DPO) have proven effective in English, they often fail to generalize robustly to multilingual settings. We propose a simple yet effective alternative, Confidence-Aware Preference Optimization (CAPO), which replaces DPO's fixed treatment of preference pairs with a dynamic loss scaling mechanism based on a relative reward. By modulating the learning signal according to the confidence in each preference pair, CAPO enhances robustness to noisy or low-margin comparisons, typically encountered in multilingual text. Empirically, CAPO outperforms existing preference optimization baselines by at least 16% in reward accuracy, and improves alignment by widening the gap between preferred and dispreferred responses across languages.

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