Margin Adaptive DPO: Leveraging Reward Model for Granular Control in Preference Optimization

arXiv:2510.05342v2 Announce Type: replace Abstract: Direct Preference Optimization (DPO) has emerged as a simple and effective method for aligning large language models. However, its reliance on a fixed temperature parameter leads to suboptimal training on diverse preference data, causing overfitting on easy examples and under-learning from informative ones. Recent methods have emerged to counter this. While IPO addresses general overfitting, its uniform regularization can be overly conservative. The more targeted approach of $\beta$-DPO suffers from its own limitations: its batch-level adapta

Source: arXiv cs.LG — read the full report at the original publisher.

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