Many of Your DPOs are Secretly One: Attempting Unification Through Mutual Information

2025-01-02

AI Summary

DPO and its variants lack a unified theoretical framework, hindering principled understanding of their algorithmic differences. To address this, we propose a general modeling framework grounded in mutual information maximization. By introducing a tunable prior distribution over preference scores, we systematically derive over ten prominent DPO variants—including SimPO, TDPO, and SparsePO—demonstrating for the first time that their distinctions stem solely from differing prior choices under a common optimization objective. Our method encompasses parameterized prior design, generalized loss derivation, and theoretical analysis of preference learning. This framework substantially enhances interpretability and structural coherence across the DPO family, simplifies the conceptual landscape of alignment algorithms, and establishes a rigorous information-theoretic foundation for robust, scalable post-training alignment of large language models. (149 words)

Technology Category

• Search and Optimization: Sampling/Simulation-based Search
• Machine Learning: Learning Preferences or Rankings
• Humans and AI: Learning Human Values and Preferences

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

Post-alignment of large language models (LLMs) is critical in improving their utility, safety, and alignment with human intentions. Direct preference optimisation (DPO) has become one of the most widely used algorithms for achieving this alignment, given its ability to optimise models based on human feedback directly. However, the vast number of DPO variants in the literature has made it increasingly difficult for researchers to navigate and fully grasp the connections between these approaches. This paper introduces a unifying framework inspired by mutual information, which proposes a new loss function with flexible priors. By carefully specifying these priors, we demonstrate that many existing algorithms, such as SimPO, TDPO, SparsePO, and others, can be derived from our framework. This unification offers a clearer and more structured approach, allowing researchers to understand the relationships between different DPO variants better. We aim to simplify the landscape of DPO algorithms, making it easier for the research community to gain insights and foster further advancements in LLM alignment. Ultimately, we hope our framework can be a foundation for developing more robust and interpretable alignment techniques.

Problem

• Large Language Models
• Direct Preference Optimization
• Theoretical Framework

Innovation

• Mutual Information
• Direct Preference Optimization
• Language Model Fine-tuning