Spectral Souping: A Unified Framework for Online Preference Alignment

TL;DR

Spectral Souping introduces a spectral representation-based framework for online preference alignment in LLMs, enabling rapid, scalable adaptation to individual user preferences without retraining.

Contribution

The paper presents a novel spectral representation discovery and a two-phase policy merging method for efficient online preference alignment in large language models.

Findings

Achieves significant performance improvements over state-of-the-art methods.

Enables rapid adaptation without costly online retraining.

Demonstrates scalability and computational efficiency.

Abstract

Reinforcement Learning from Human Feedback (RLHF) effectively aligns Large Language Models (LLMs) with aggregate human preferences but often fails to address the diverse and conflicting needs of individual users. To overcome this issue, we introduce Spectral Souping, a unified framework for efficient, online preference alignment. Our contribution is the discovery of a universal spectral representation within LLMs, which is proven to be highly amenable to model merging. This theoretical insight enables a two-phase methodology: we first learn a basis of specialized policies offline, each focused on a distinct, fine-grained preference dimension. An online adaptation algorithm then efficiently ``soups'' these policies at inference time, either by merging their outputs or parameters, enabling rapid model adaptation without the need for costly online retraining w.r.t. tailored preference rewards. Experiments on online preference alignment benchmarks demonstrate that our method achieves significant performance improvements over existing state-of-the-art approaches, presenting a scalable and computationally efficient solution for dynamically adapting LLMs to individual user preferences.