HelpSteer2 — LLM Preference (RLHF / DPO) Dataset

NVIDIA's 21K open-source preference dataset designed for training reward models and RLHF. Each response is annotated by human raters on 5 dimensions: helpfulness, correctness, coherence, complexity, and verbosity. Significantly improves reward model accuracy over HH-RLHF.

Dataset Details

Providernvidia
CategoryPreference (RLHF / DPO)
Size21K Samples
LicenseCC BY 4.0
Downloads290k
TagsRLHF, Reward Model, Multi-aspect, NVIDIA, Human-rated
 
from datasets import load_dataset
ds = load_dataset("nvidia/HelpSteer2")

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Estimated VRAM to fine-tune with QLoRA (4-bit base model + LoRA adapters), using conservative defaults:

7B QLoRA~6GB VRAM
13B QLoRA~10GB VRAM

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Frequently asked questions

Can I use HelpSteer2 commercially?
Yes — HelpSteer2 is released under CC BY 4.0, a permissive license that allows commercial use, including training models you ship in a product. Check the dataset card for attribution requirements before release.
How much data does HelpSteer2 contain, and do I need all of it?
HelpSteer2 contains 21K Samples. You rarely need all of it: for style and format fine-tuning, a few hundred to a few thousand examples are enough — load a slice (e.g. split="train[:1000]") and scale up only if quality plateaus.
What is HelpSteer2 best used for?
Training reward models with fine-grained quality ratings. It belongs to the Preference (RLHF / DPO) section of our dataset hub, where you'll find alternatives and complementary sets.

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