HelpSteer3-Preference: Open Human-Annotated Preference Data across
  Diverse Tasks and Languages
Authors: name: Zhilin Wang, name: Jiaqi Zeng, name: Olivier Delalleau, name: Hoo-Chang Shin, name: Felipe Soares, name: Alexander Bukharin, name: Ellie Evans, name: Yi Dong, name: Oleksii Kuchaiev

Preference datasets are essential for training general-domain,
instruction-following language models with Reinforcement Learning from Human
Feedback (RLHF). Each subsequent data release raises expectations for future
data collection, meaning there is a constant need to advance the quality and
diversity of openly available preference data. To address this need, we
introduce HelpSteer3-Preference, a permissively licensed (CC-BY-4.0),
high-quality, human-annotated preference dataset comprising of over 40,000
samples. These samples span diverse real-world applications of large language
models (LLMs), including tasks relating to STEM, coding and multilingual
scenarios. Using HelpSteer3-Preference, we train Reward Models (RMs) that
achieve top performance on RM-Bench (82.4%) and JudgeBench (73.7%). This
represents a substantial improvement (~10% absolute) over the previously
best-reported results from existing RMs. We demonstrate HelpSteer3-Preference
can also be applied to train Generative RMs and how policy models can be
aligned with RLHF using our RMs. Dataset (CC-BY-4.0):
https://huggingface.co/datasets/nvidia/HelpSteer3#preference