HelpSteer2-Preference: Complementing Ratings with Preferences
Authors: name: Zhilin Wang, name: Alexander Bukharin, name: Olivier Delalleau, name: Daniel Egert, name: Gerald Shen, name: Jiaqi Zeng, name: Oleksii Kuchaiev, name: Yi Dong

Reward models are critical for aligning models to follow instructions, and
are typically trained following one of two popular paradigms: Bradley-Terry
style or Regression style. However, there is a lack of evidence that either
approach is better than the other, when adequately matched for data. This is
primarily because these approaches require data collected in different (but
incompatible) formats, meaning that adequately matched data is not available in
existing public datasets. To tackle this problem, we release preference
annotations (designed for Bradley-Terry training) to complement existing
ratings (designed for Regression style training) in the HelpSteer2 dataset. To
improve data interpretability, preference annotations are accompanied with
human-written justifications. Using this data, we conduct the first
head-to-head comparison of Bradley-Terry and Regression models when adequately
matched for data. Based on insights derived from such a comparison, we propose
a novel approach to combine Bradley-Terry and Regression reward modeling. A
Llama-3.1-70B-Instruct model tuned with this approach scores 94.1 on
RewardBench, emerging top of more than 140 reward models as of 1 Oct 2024. We
also demonstrate the effectiveness of this reward model at aligning models to
follow instructions in RLHF. We open-source this dataset (CC-BY-4.0 license) at
https://huggingface.co/datasets/nvidia/HelpSteer2 and openly release the
trained Reward Model at
https://huggingface.co/nvidia/Llama-3.1-Nemotron-70B-Reward