One Adapts to Any: Meta Reward Modeling for Personalized LLM Alignment

TL;DR

This paper introduces Meta Reward Modeling, a meta-learning approach for personalized LLM alignment that efficiently adapts to individual user preferences with limited feedback, outperforming existing methods.

Contribution

It proposes a novel meta-learning framework for personalized reward models, enabling rapid adaptation and robustness across diverse users.

Findings

MRM improves few-shot personalization performance.

MRM enhances robustness to user variability.

Code is available at https://github.com/ModalityDance/MRM.

Abstract

Alignment of Large Language Models (LLMs) aims to align outputs with human preferences, and personalized alignment further adapts models to individual users. This relies on personalized reward models that capture user-specific preferences and automatically provide individualized feedback. However, developing these models faces two critical challenges: the scarcity of feedback from individual users and the need for efficient adaptation to unseen users. We argue that addressing these constraints requires a paradigm shift from fitting data to learn user preferences to learn the process of preference adaptation. To realize this, we propose Meta Reward Modeling (MRM), which reformulates personalized reward modeling as a meta-learning problem. Specifically, we represent each user's reward model as a weighted combination of base reward functions, and optimize the initialization of these weights using a Model-Agnostic Meta-Learning (MAML)-style framework to support fast adaptation under limited feedback. To ensure robustness, we introduce the Robust Personalization Objective (RPO), which places greater emphasis on hard-to-learn users during meta optimization. Extensive experiments on personalized preference datasets validate that MRM enhances few-shot personalization, improves user robustness, and consistently outperforms baselines. We release code at https://github.com/ModalityDance/MRM.