KL-Regularized RLHF with Multiple Reference Models: Exact Solutions and Sample Complexity

TL;DR

This paper provides the first exact solution and theoretical analysis for integrating multiple reference models into KL-regularized RLHF, enhancing LLM alignment by leveraging diverse models with proven sample complexity guarantees.

Contribution

It introduces an exact solution to the multiple reference model problem in RLHF and extends analysis to forward KL, offering new theoretical insights and sample complexity bounds.

Findings

First exact solution for multiple reference models in RLHF

Sample complexity guarantees for reverse and forward KL-regularized RLHF

Theoretical framework enabling better LLM alignment techniques

Abstract

Recent methods for aligning large language models (LLMs) with human feedback predominantly rely on a single reference model, which limits diversity, model overfitting, and underutilizes the wide range of available pre-trained models. Incorporating multiple reference models has the potential to address these limitations by broadening perspectives, reducing bias, and leveraging the strengths of diverse open-source LLMs. However, integrating multiple reference models into reinforcement learning with human feedback (RLHF) frameworks poses significant theoretical challenges, where achieving exact solutions has remained an open problem. This paper presents the first \emph{exact solution} to the multiple reference model problem in reverse KL-regularized RLHF. We introduce a comprehensive theoretical framework that includes rigorous statistical analysis and provides sample complexity guarantees. Additionally, we extend our analysis to forward KL-regularized RLHF, offering new insights into sample complexity requirements in multiple reference scenarios. Our contributions lay the foundation for more advanced and adaptable LLM alignment techniques, enabling the effective use of multiple reference models. This work paves the way for developing alignment frameworks that are both theoretically sound and better suited to the challenges of modern AI ecosystems.