HelpSteer2
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.
from datasets import load_dataset
ds = load_dataset("nvidia/HelpSteer2")Preview a sample row
{
"prompt": "How do I improve my sleep quality?",
"response": "To improve sleep quality: 1) Maintain a consistent sleep schedule 2) Create a dark, cool environment 3) Avoid screens 1hr before bed...",
"helpfulness": 4,
"correctness": 5,
"coherence": 5,
"complexity": 2,
"verbosity": 3
}Fine-tune with this dataset
Estimated VRAM to fine-tune with QLoRA (4-bit base model + LoRA adapters), using conservative defaults:
New to fine-tuning? Follow the step-by-step walkthrough: Fine-Tune Your First LLM in 1 Hour
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.