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reasoning/natural_reasoning/README.md

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+---
+license: cc-by-nc-4.0
+task_categories:
+- text-generation
+language:
+- en
+pretty_name: Natural Reasoning
+size_categories:
+- 1M<n<10M
+---
+[NaturalReasoning](https://arxiv.org/abs/2502.13124) is a large-scale dataset for general reasoning tasks. It consists of high-quality challenging reasoning questions backtranslated from pretraining corpora [DCLM](https://github.com/mlfoundations/dclm) and [FineMath](https://huggingface.co/datasets/HuggingFaceTB/finemath). The questions have been deduplicated and decontaminated from popular reasoning benchmarks including MATH, GPQA, MMLU-Pro, MMLU-STEM. For each question, we extract the reference final answer from the original document from the pretraining corpora if possible. We also provide a model-generated response from Llama3.3-70B-Instruct.
+
+We release a 1.1 million subset of NaturalReasoning to the research community to foster research on training strong LLM reasoners. 
+
+You can load the dataset as follows
+```python
+from datasets import load_dataset
+
+ds = load_dataset("facebook/natural_reasoning")
+```
+
+For more information regarding data collection, please refer to our [paper](https://arxiv.org/abs/2502.13124).
+
+
+
+## Reference Answer Statistics
+In the 1.1 million subset, 18.29% of the questions do not have a reference answer, 9.71% of the questions have a single word answer, 21.58% of the questions have a short answer while 50.42% of the questions have a long reference answer.
+
+
+## Scaling Curve
+Training on NaturalReasoning shows better scaling effects than training on other datasets when training Llama3.1-8B-Instruct model. In particular, we measure the average performance on three benchmarks: MATH, GPQA, MMLU-Pro. 
+
+<img src="https://cdn-uploads.huggingface.co/production/uploads/659a395421a7431643caedda/S6aO-agjRRhc0JLkohZ5z.jpeg" style="width:50%; max-width:400px;">
+
+
+
+## Citation
+If you use data from NaturalReasoning, please cite with the following BibTex entry:
+```
+@misc{yuan2025naturalreasoningreasoningwild28m,
+      title={NaturalReasoning: Reasoning in the Wild with 2.8M Challenging Questions}, 
+      author={Weizhe Yuan and Jane Yu and Song Jiang and Karthik Padthe and Yang Li and Dong Wang and Ilia Kulikov and Kyunghyun Cho and Yuandong Tian and Jason E Weston and Xian Li},
+      year={2025},
+      eprint={2502.13124},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL},
+      url={https://arxiv.org/abs/2502.13124}, 
+}
+```