MARS: Margin-Aware Reward-Modeling with Self-Refinement

TL;DR

MARS introduces an adaptive augmentation method that focuses on uncertain preference pairs to improve reward model robustness, backed by theoretical guarantees and empirical gains.

Contribution

It proposes a novel margin-aware augmentation strategy that targets ambiguous data points, enhancing reward model training efficiency and robustness.

Findings

Consistent performance improvements over uniform augmentation.

Theoretical guarantees on increased loss function curvature.

Enhanced reward model robustness through targeted data augmentation.

Abstract

Reward modeling is a core component of modern alignment pipelines including RLHF and RLAIF, underpinning policy optimization methods including PPO and TRPO. However, training reliable reward models relies heavily on human-labeled preference data, which is costly and limited, motivating the use of data augmentation. Existing augmentation approaches typically operate at the representation or semantic level and remain agnostic to the reward model's estimation difficulty. In this paper, we propose MARS, an adaptive, margin-aware augmentation and sampling strategy that explicitly targets ambiguous and failure modes of the reward model. Our proposed framework, MARS, concentrates augmentation on low-margin (ambiguous) preference pairs where the reward model is most uncertain, and iteratively refines the training distribution via hard-sample augmentation. We provide theoretical guarantees showing that this strategy increases the average curvature of the loss function hence enhance information and improves conditioning, along with empirical results demonstrating consistent gains over uniform augmentation for robust reward modeling.