From RLHF to Direct Alignment: A Theoretical Unification of Preference Learning for Large Language Models

TL;DR

This paper unifies various preference learning methods for aligning large language models through a theoretical framework, clarifying their differences and guiding practitioners in method selection.

Contribution

It provides a formal unification of preference learning approaches, characterizing them along three axes and establishing key theoretical results and failure modes.

Findings

Reveals the underlying structure of preference learning methods.

Establishes scaling laws and conditions for method failure.

Provides a decision guide for practitioners.

Abstract

Aligning large language models (LLMs) with human preferences has become essential for safe and beneficial AI deployment. While Reinforcement Learning from Human Feedback (RLHF) established the dominant paradigm, a proliferation of alternatives -- Direct Preference Optimization (DPO), Identity Preference Optimization (IPO), Kahneman-Tversky Optimization (KTO), Simple Preference Optimization (SimPO), and many others -- has left practitioners without clear guidance on method selection. This survey provides a \textit{theoretical unification} of preference learning methods, revealing that the apparent diversity reduces to principled choices along three orthogonal axes: \textbf{(I) Preference Model} (what likelihood model underlies the objective), \textbf{(II) Regularization Mechanism} (how deviation from reference policies is controlled), and \textbf{(III) Data Distribution} (online vs.\ offline learning and coverage requirements). We formalize each axis with precise definitions and theorems, establishing key results including the coverage separation between online and offline methods, scaling laws for reward overoptimization, and conditions under which direct alignment methods fail. Our analysis reveals that failure modes -- length hacking, mode collapse, likelihood displacement -- arise from specific, predictable combinations of design choices. We synthesize empirical findings across 50+ papers and provide a practitioner's decision guide for method selection. The framework transforms preference learning from an empirical art into a theoretically grounded discipline.