Dataset Card for reasoning

This dataset has been created with distilabel.

  Dataset Summary

This dataset contains a pipeline.yaml which can be used to reproduce the pipeline that generated it in distilabel using the distilabel CLI: distilabel pipeline run --config "https://huggingface.co/datasets/dvilasuero/reasoning/raw/main/pipeline.yaml"

or explore the configuration: distilabel pipeline info --config… See the full description on the dataset page: https://huggingface.co/datasets/dvilasuero/reasoning.

NaturalReasoning is a large-scale dataset for general reasoning tasks. It consists of high-quality challenging reasoning questions backtranslated from pretraining corpora DCLM and 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… See the full description on the dataset page: https://huggingface.co/datasets/facebook/natural_reasoning.

Natural Reasoning Dataset

Source: Huggingface

Dataset Overview

Natural Reasoning is a large-scale dataset designed for general reasoning tasks. It consists of high-quality, challenging reasoning questions backtranslated from pretraining corpora DCLM and FineMath. The dataset has been carefully deduplicated and decontaminated from popular reasoning benchmarks including MATH, GPQA, MMLU-Pro, and MMLU-STEM.

A 1.1 million subset of the Natural Reasoning dataset is released to the research community to foster the development of strong large language model (LLM) reasoners.

Dataset Information

File Format: natural_reasoning.parquet

Click here to view the dataset

• 📝 License: CC-BY-NC-4.0
• 🧠 Task Categories: Text Generation Reasoning
• 🌐 Language: English (en)
• 📊 Dataset Size: 1M < n < 10M
• 📥 Source: Hugging Face
• 📄 Original Paper: NaturalReasoning: Reasoning in the Wild with 2.8M Challenging Questions

How to Use

You can load the dataset directly from Hugging Face as follows:

from datasets import load_dataset

ds = load_dataset("facebook/natural_reasoning")

Data Collection and Quality

The dataset was constructed from the pretraining corpora DCLM and FineMath. The questions have been filtered to remove contamination and duplication from widely-used reasoning benchmarks like MATH, GPQA, MMLU-Pro, and MMLU-STEM. For each question, the dataset provides a reference final answer extracted from the original document when available, and also includes a model-generated response from Llama3.3-70B-Instruct.

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. - 50.42% of the questions have a long-form reference answer.

Scaling Curve Performance

Training on the Natural Reasoning dataset shows superior scaling effects compared to other datasets. When training the Llama3.1-8B-Instruct model, the dataset achieved better performance on average across three key benchmarks: MATH, GPQA, and MMLU-Pro.

https://cdn-uploads.huggingface.co/production/uploads/659a395421a7431643caedda/S6aO-agjRRhc0JLkohZ5z.jpeg" alt="Scaling Curve">

Citation

If you use the Natural Reasoning dataset, please cite it 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}
}

Source: Hugging Face

reasoning-0.01 subset

synthetic dataset of reasoning chains for a wide variety of tasks. we leverage data like this across multiple reasoning experiments/projects. stay tuned for reasoning models and more data. Thanks to Hive Digital Technologies (https://x.com/HIVEDigitalTech) for their compute support in this project and beyond.

reedmayhew/claude-3.7-sonnet-reasoning dataset hosted on Hugging Face and contributed by the HF Datasets community

Dataset Description

ReMI was introduced in ReMI: A Dataset for Reasoning with Multiple Images. It contains 13 tasks namely: EmojiAlgebra, FuncRead, GeomShape, GeomCost, Collisions, Clocks, Schedule, Charts, CodeEdit, Isomorphism, Maps, RefCOCO, and IQ.

  Dataset Usage





  Data Downloading

All the data examples were divided into two subsets: train and test.

train: contains 2 examples per task (26 in total) to be used as fewshot examples. test: contains 200 examples… See the full description on the dataset page: https://huggingface.co/datasets/mehrankazemi/ReMI.

News

[2025/04/22] We split the data and kept only the medical SFT dataset (medical_o1_sft.json). The file medical_o1_sft_mix.json contains a mix of medical and general instruction data. [2025/02/22] We released the distilled dataset from Deepseek-R1 based on medical verifiable problems. You can use it to initialize your models with the reasoning chain from Deepseek-R1. [2024/12/25] We open-sourced the medical reasoning dataset for SFT, built on medical verifiable problems and an LLM… See the full description on the dataset page: https://huggingface.co/datasets/FreedomIntelligence/medical-o1-reasoning-SFT.

MME-Reasoning 🔥: A Comprehensive Benchmark for Logical Reasoning in MLLMs

Official repository for "MME-Reasoning: A Comprehensive Benchmark for Logical Reasoning in MLLMs". 🌟 For more details, please refer to the project page. [🚀Project Page] [📖 Paper] [🗃️ Github] [🏆 Leaderboard]

  💥 News

[2025.05.23] 🔥 We launch MME-Reasoning, a comprehensive benchmark designed to evaluate the reasoning ability of MLLMs. We release the arxiv paper and all data samples… See the full description on the dataset page: https://huggingface.co/datasets/U4R/MME-Reasoning.

Rhushya/synthetic-reasoning-dataset-llama3-1 dataset hosted on Hugging Face and contributed by the HF Datasets community

Menlo/Maze-Reasoning dataset hosted on Hugging Face and contributed by the HF Datasets community

lccccc-1/Multimodal-Visual-Reasoning-Dataset dataset hosted on Hugging Face and contributed by the HF Datasets community

Dataset Card for my-distiset-986461

This dataset has been created with distilabel.

  Dataset Summary

This dataset contains a pipeline.yaml which can be used to reproduce the pipeline that generated it in distilabel using the distilabel CLI: distilabel pipeline run --config "https://huggingface.co/datasets/sdiazlor/my-distiset-986461/raw/main/pipeline.yaml"

or explore the configuration: distilabel pipeline info --config… See the full description on the dataset page: https://huggingface.co/datasets/sdiazlor/python-reasoning-dataset.

CodeI/O: Condensing Reasoning Patterns via Code Input-Output Prediction

📑 Paper  |  🌐 Project Page  |  💾 Released Resources  |  📦 Repo 

This is the resource page of the CodeI/O collection on Huggingface, we highlight your currect position with a blue block. Dataset

  Dataset
  Link


  CodeI/O-PythonEdu-Reasoning

   🤗

Please also check the raw data after our processing if you are interested:… See the full description on the dataset page: https://huggingface.co/datasets/hkust-nlp/CodeIO-PyEdu-Reasoning.

The Visual Spatial Reasoning (VSR) corpus is a collection of caption-image pairs with true/false labels. Each caption describes the spatial relation of two individual objects in the image, and a vision-language model (VLM) needs to judge whether the caption is correctly describing the image (True) or not (False).

Dataset Card for "livebench/reasoning"

LiveBench is a benchmark for LLMs designed with test set contamination and objective evaluation in mind. It has the following properties:

LiveBench is designed to limit potential contamination by releasing new questions monthly, as well as having questions based on recently-released datasets, arXiv papers, news articles, and IMDb movie synopses. Each question has verifiable, objective ground-truth answers, allowing hard questions to be scored… See the full description on the dataset page: https://huggingface.co/datasets/livebench/reasoning.

Shuffled mix of:

Large high dataset of quality web text: https://huggingface.co/datasets/EleutherAI/fineweb-edu-dedup-10b Medium dataset of QwQ math reasoning: https://huggingface.co/datasets/PrimeIntellect/NuminaMath-QwQ-CoT-5M Small dataset of DeepSeek-R1 reasoning traces on math, coding, science and puzzle data: https://huggingface.co/datasets/bespokelabs/Bespoke-Stratos-17k

Intended for disentanglement of advanced reasoning models (SAEs, transcoders). Generation code:… See the full description on the dataset page: https://huggingface.co/datasets/EleutherAI/reasoning-mix.

Synthetic reasoning dataset

Original version:

https://huggingface.co/datasets/lighteval/synthetic_reasoning

Translation source code: https://github.com/martinakaduc/ura-llama/tree/main/dataset_scripts/custom_datasets

[!NOTE] We have released a paper for OpenThoughts! See our paper here.

  Open-Thoughts-114k

Open synthetic reasoning dataset with 114k high-quality examples covering math, science, code, and puzzles! Inspect the content with rich formatting with Curator Viewer.

  Available Subsets

default subset containing ready-to-train data used to finetune the OpenThinker-7B and OpenThinker-32B models: ds = load_dataset("open-thoughts/OpenThoughts-114k", split="train")… See the full description on the dataset page: https://huggingface.co/datasets/open-thoughts/OpenThoughts-114k.

zou-lab/MedCaseReasoning dataset hosted on Hugging Face and contributed by the HF Datasets community

PhysReason: A Comprehensive Benchmark towards Physics-Based Reasoning

PhysReason is accepted by ACL-2025-main

  📋 Overview

PhysReason is a comprehensive physics-based reasoning benchmark consisting of 1,200 physics problems spanning multiple domains, with a focus on both knowledge-based (25%) and reasoning-based (75%) questions. This benchmark addresses the critical gap in evaluating large language models' capabilities in physics-based reasoning, which requires… See the full description on the dataset page: https://huggingface.co/datasets/zhibei1204/PhysReason.