AI RESEARCH
Intrinsic Mutual Information as a Modulator for Preference Optimization
arXiv CS.LG
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ArXi:2604.24804v1 Announce Type: new Offline preference optimization methods, such as Direct Preference Optimization (DPO), offer significant advantages in aligning Large Language Models (LLMs) with human values. However, achieving optimal performance with these methods typically involves additional hyperparameter tuning, resulting in substantial time overhead. Although prior work has proposed a range of improvements, these methods remain limited in effectiveness and have not fully eliminated reliance on hyperparameter tuning.