Improving LLM General Preference Alignment via Optimistic Online Mirror Descent

TL;DR

This paper introduces an optimistic online mirror descent method for aligning large language models with human preferences, removing restrictive assumptions and achieving better theoretical and empirical results than existing RLHF approaches.

Contribution

It proposes a novel alignment framework that drops the Bradley-Terry model assumption and leverages game theory, with improved theoretical guarantees and superior experimental performance.

Findings

Achieves an $O(T^{-1})$ duality gap bound, better than previous methods.

Outperforms state-of-the-art RLHF algorithms on multiple benchmarks.

Provides a theoretically grounded approach to modeling complex human preferences.

Abstract

Reinforcement learning from human feedback (RLHF) has demonstrated remarkable effectiveness in aligning large language models (LLMs) with human preferences. Many existing alignment approaches rely on the Bradley-Terry (BT) model assumption, which assumes the existence of a ground-truth reward for each prompt-response pair. However, this assumption can be overly restrictive when modeling complex human preferences. In this paper, we drop the BT model assumption and study LLM alignment under general preferences, formulated as a two-player game. Drawing on theoretical insights from learning in games, we integrate optimistic online mirror descent into our alignment framework to approximate the Nash policy. Theoretically, we demonstrate that our approach achieves an $O(T^{-1})$ bound on the duality gap, improving upon the previous $O(T^{-1/2})$ result. More importantly, we implement our method and show through experiments that it outperforms state-of-the-art RLHF algorithms across multiple representative benchmarks.