HelpSteer2-Preference: Complementing Ratings with Preferences

ICLR 2025 Conference Submission12434 Authors

Keywords: reward modelling, rlhf, model alignment

TL;DR: We release data to support a data-matched comparison of Bradley-Terry and Regression style Reward Models, which inspires an approach resulting in a Reward Model scoring 94.1% on RewardBench, emerging the top model out of 140+ models as of 1 Oct 2024.

Abstract:

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.
This reward model can then be used with REINFORCE to align a model to reach 85.0 on Arena Hard, which is No. 1 as of 1 Oct 2024.
We open-source this dataset (CC-BY-4.0 license) and openly release the trained reward and aligned models.

Primary Area: datasets and benchmarks

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Submission Number: 12434