  High-quality preference datasets are essential for training reward models
that can effectively guide large language models (LLMs) in generating
high-quality responses aligned with human preferences. As LLMs become stronger
and better aligned, permissively licensed preference datasets, such as Open
Assistant, HH-RLHF, and HelpSteer need to be updated to remain effective for
reward modeling. Methods that distil preference data from proprietary LLMs such
as GPT-4 have restrictions on commercial usage imposed by model providers. To
improve upon both generated responses and attribute labeling quality, we
release HelpSteer2, a permissively licensed preference dataset (CC-BY-4.0).
Using a powerful internal base model trained on HelpSteer2, we are able to
achieve the SOTA score (92.0%) on Reward-Bench's primary dataset, outperforming
currently listed open and proprietary models, as of June 12th, 2024. Notably,
HelpSteer2 consists of only ten thousand response pairs, an order of magnitude
fewer than existing preference datasets (e.g., HH-RLHF), which makes it highly
efficient for training reward models. Our extensive experiments demonstrate
that reward models trained with HelpSteer2 are effective in aligning LLMs. In
particular, we propose SteerLM 2.0, a model alignment approach that can
effectively make use of the rich multi-attribute score predicted by our reward
models. HelpSteer2 is available at
https://huggingface.co/datasets/nvidia/HelpSteer2 and code is available at
https://github.com/NVIDIA/NeMo-Aligner
