OpenCodeReasoning — LLM Reasoning Dataset
The largest open reasoning dataset for code: 735,255 Python samples distilled from DeepSeek R1 across 28,319 unique competitive-programming problems from CodeForces, LeetCode, AtCoder, and more. SFT-only training on it reached 61.8% on LiveCodeBench — beating RL-trained alternatives. Predominantly CC BY 4.0 with some Apache 2.0/MIT-sourced subsets.
Dataset Details
| Provider | NVIDIA |
| Category | Reasoning |
| Size | 735k Samples |
| License | CC BY 4.0 |
| Downloads | n/a |
| Tags | Code-Reasoning, Competitive-Programming, R1-Distilled, Python, 2025 |
from datasets import load_dataset
ds = load_dataset("nvidia/OpenCodeReasoning")
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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 OpenCodeReasoning commercially?
Yes — OpenCodeReasoning 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 OpenCodeReasoning contain, and do I need all of it?
OpenCodeReasoning contains 735k 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 OpenCodeReasoning best used for?
Fine-tuning coding models that show their reasoning before writing the solution. It belongs to the Reasoning section of our dataset hub, where you'll find alternatives and complementary sets.
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