s1K-1.1 — LLM Reasoning Dataset

Just 1,000 ultra-curated questions with reasoning traces — selected from 59k candidates for difficulty, diversity, and quality, with traces regenerated by DeepSeek R1 in the 1.1 release. Fine-tuning Qwen2.5-32B on s1K (26 minutes on 16 H100s) plus 'budget forcing' at inference exceeded o1-preview on competition math. The LIMA of reasoning: proof that data quality can beat quantity.

Dataset Details

ProviderSimpleScaling
CategoryReasoning
Size1k Rows
LicenseApache 2.0
Downloadsn/a
TagsCurated, Test-Time-Scaling, Quality-over-Quantity, R1-Traces, 2025
from datasets import load_dataset
ds = load_dataset("simplescaling/s1K-1.1")

Fine-tune with this dataset

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

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

Check if your GPU can fine-tune this →

New to fine-tuning? Follow the step-by-step walkthrough: Fine-Tune Your First LLM in 1 Hour

Related datasets

Frequently asked questions

Can I use s1K-1.1 commercially?
Yes — s1K-1.1 is released under Apache 2.0, 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 s1K-1.1 contain, and do I need all of it?
s1K-1.1 contains 1k Rows. 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 s1K-1.1 best used for?
Cheap, fast reasoning fine-tunes — 1k samples means minutes of training, not days. It belongs to the Reasoning section of our dataset hub, where you'll find alternatives and complementary sets.

← All datasets | Fine-Tuning Guide