ROPO: Robust Preference Optimization for Large Language Models

The prevalent noise in the preference data unavoidably poses significant challenges to the preference alignment of large language models (LLMs). Existing efforts for this problem either marginally alleviate the impact of noise without noise reduction, or rely on external LLMs that incur substantial computational costs. To address these challenges, we propose RObust Preference Optimization (ROPO), an iterative alignment approach that integrates noise-tolerance and noise filtering without the aid of external models. Specifically, ROPO first formulates the training process with adaptive noise reduction as an optimization problem, which can be efficiently solved in an iterative paradigm. Then, to equip this solving process with noise-tolerance and noiseidentification capabilities, we derive a robust loss that suppresses the gradients from samples with high uncertainty. We demonstrate both empirically and theoretically that the derived loss is key to the noise-tolerance and effective filtering of noisy samples. The derived loss further inspires a robustness-guided rejection sampling technique to compensate for the potential important information in discarded queries. Extensive experiments on several widely-used datasets and model architectures demonstrate that ROPO significantly outperforms all baselines under four practical noise settings and the random symmetric noise, with its advantage increasing as the noise rate increases.