reWordBench: Benchmarking and Improving the Robustness of Reward Models
  with Transformed Inputs
Authors: name: Zhaofeng Wu, name: Michihiro Yasunaga, name: Andrew Cohen, name: Yoon Kim, name: Asli Celikyilmaz, name: Marjan Ghazvininejad

Reward models have become a staple in modern NLP, serving as not only a
scalable text evaluator, but also an indispensable component in many alignment
recipes and inference-time algorithms. However, while recent reward models
increase performance on standard benchmarks, this may partly be due to
overfitting effects, which would confound an understanding of their true
capability. In this work, we scrutinize the robustness of reward models and the
extent of such overfitting. We build **reWordBench**, which systematically
transforms reward model inputs in meaning- or ranking-preserving ways. We show
that state-of-the-art reward models suffer from substantial performance
degradation even with minor input transformations, sometimes dropping to
significantly below-random accuracy, suggesting brittleness. To improve reward
model robustness, we propose to explicitly train them to assign similar scores
to paraphrases, and find that this approach also improves robustness to other
distinct kinds of transformations. For example, our robust reward model reduces
such degradation by roughly half for the Chat Hard subset in RewardBench.
Furthermore, when used in alignment, our robust reward models demonstrate
better utility and lead to higher-quality outputs, winning in up to 59% of
instances against a standardly trained RM.