datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
Math-Reasoning
Math-Reasoning
Dataset Description
Mathematical problem-solving, rewriting, and dialogue data for reasoning-oriented language-model training. This repository is part of the K2 Horizon collection.
The repository is organized into multiple subsets. Every subset has a train split backed by Parquet shards, which supports Dataset Viewer inspection and streaming access.
K2 Horizon Dataset Series
Dataset repository
Focus
Subsets
IFM/TxT360-v2… See the full description on the dataset page: https://huggingface.co/datasets/IFM/Math-Reasoning.Code-Reasoning
Code-Reasoning
Dataset Description
Code problem-solving data with reasoning, direct-answer, and task-synthesis subsets. This repository is part of the K2 Horizon collection.
The repository is organized into multiple subsets. Every subset has a train split backed by Parquet shards, which supports Dataset Viewer inspection and streaming access.
K2 Horizon Dataset Series
Dataset repository
Focus
Subsets
IFM/TxT360-v2
Web and… See the full description on the dataset page: https://huggingface.co/datasets/IFM/Code-Reasoning.medical-o1-reasoning-SFT
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… See the full description on the dataset page: https://huggingface.co/datasets/FreedomIntelligence/medical-o1-reasoning-SFT.FLUX-Reason-6M
FLUX-Reason-6M
FLUX-Reason-6M is a massive, 6-million-scale text-to-image dataset engineered to instill complex reasoning capabilities in generative models. This dataset was created to bridge the performance gap between open-source and leading closed-source text-to-image systems.
This dataset contains:
6 million high-quality, reasoning-focused images synthesized by the state-of-the-art FLUX.1-dev model.
20 million bilingual (English and Chinese) descriptions, providing a rich… See the full description on the dataset page: https://huggingface.co/datasets/LucasFang/FLUX-Reason-6M.FinMMDocRZebra-CoT
Zebra‑CoT
A diverse large-scale dataset for interleaved vision‑language reasoning traces.
Dataset Description
Zebra‑CoT is a diverse large‑scale dataset with 182,384 samples containing logically coherent interleaved text‑image reasoning traces across four major categories: scientific reasoning, 2D visual reasoning, 3D visual reasoning, and visual logic & strategic games.
Dataset Structure
Each example in Zebra‑CoT consists of:
Problem statement:… See the full description on the dataset page: https://huggingface.co/datasets/multimodal-reasoning-lab/Zebra-CoT.reasoning
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.qwen_trajectories_final3d-spatial-reasoning-13d-spatial-reasoningstagingRaw generator output behind procedural-pile, one configuration per build, before deduplication and the train/test split.
load_dataset("reasoning-core/staging", "rc14", split="train", streaming=True)
chankhavu-imo-reasoning-tracesskillgym-test
SkillGym test set
400 agent tasks that each require one agent skill, in Harbor task format.
Layout
tasks/
procedural/
held_in/ 50 tasks
held_out/ 50 tasks
constraint_satisfaction/ (same)
abductive/ (same)
partial_order/ (same)
manifest.jsonl one row per task
Each profile has 50 held-in and 50 held-out tasks (held-in = the task's skill also appears in the
training data, held-out =… See the full description on the dataset page: https://huggingface.co/datasets/reasonwang/skillgym-test.3d-spatial-reasoning-2Fable-5.1-Max-Reasoning-Filtered-10000x
Dataset Description
This dataset contains 10,000 agentic coding and reasoning multi-turn high-quality traces generated by the new Fable 5.1 model using max reasoning effort.
It holds almost 500,000,000 tokens of step-by-step chain-of-thought programming across multiple complex domains.
It has also been deduplicated and heavily filtered to remove low-quality traces, keeping only high-quality traces.
Dataset Statistics
Metric
Value
Total Examples
10,000… See the full description on the dataset page: https://huggingface.co/datasets/MoreThought/Fable-5.1-Max-Reasoning-Filtered-10000x.SFT-Reasoning
SFT-Reasoning
Dataset Description
Instruction-following and reasoning data prepared for supervised fine-tuning. This repository is part of the K2 Horizon collection.
The repository is organized into multiple subsets. Every subset has a train split backed by Parquet shards, which supports Dataset Viewer inspection and streaming access.
K2 Horizon Dataset Series
Dataset repository
Focus
Subsets
IFM/TxT360-v2
Web and question-answering text… See the full description on the dataset page: https://huggingface.co/datasets/IFM/SFT-Reasoning.SWE-Bench-Verified-O1-reasoning-high-results
SWE-Bench Verified O1 Dataset
Executive Summary
This repository contains verified reasoning traces from the O1 model evaluating software engineering tasks. Using OpenHands + CodeAct v2.2, we tested O1's bug-fixing capabilities on the SWE-Bench Verified dataset, achieving a 28.8% success rate across 500 test instances.
Overview
This dataset was generated using the CodeAct framework, which aims to improve code generation through enhanced action-based reasoning.… See the full description on the dataset page: https://huggingface.co/datasets/AlexCuadron/SWE-Bench-Verified-O1-reasoning-high-results.Edge-Agent-Reasoning-WebSearch-260K
Edge Agent Reasoning WebSearch 260K
Abstract
The Edge-Agent-Reasoning-WebSearch-260K dataset is a massive, synthetically expert-engineered corpus of over 700 Million tokens, designed to train small, local models (SLMs) and edge-deployed agents in advanced problem deconstruction and self-aware reasoning.
Rather than training a model to execute instructions directly—which often leads to hallucinations when context is missing—this dataset trains a model to act as a… See the full description on the dataset page: https://huggingface.co/datasets/yatin-superintelligence/Edge-Agent-Reasoning-WebSearch-260K.copying_reasoning_task_improved
copying_reasoning_task_improved
Dataset Description
The Enhanced Copying Reasoning Task Dataset is designed to provide a rich resource for analyzing promotional texts and their key elements. This dataset includes a variety of question-and-answer formats, focusing on whether specific phrases are mentioned within the text. Its purpose is to assist in the training of models for natural language understanding tasks, particularly in identifying relevant information in… See the full description on the dataset page: https://huggingface.co/datasets/Mobiusi/copying_reasoning_task_improved.Reason_Tuning
🎨 UniReason • Unified Reasoning Framework for World Knowledge–Aligned Image Generation and Editing
UniReason is a unified framework that harmonizes text-to-image generation and image editing through a dual reasoning paradigm. We formulate generation as world knowledge-enhanced planning to inject implicit constraints, and leverage editing capabilities for fine-grained visual refinement to further correct visual errors via self-reflection. This approach… See the full description on the dataset page: https://huggingface.co/datasets/Alex11556666/Reason_Tuning.AIME_1983_2024-Reasoning-Paths
News
🌟🌟🌟 Try this dataset in our HuggingFace Space!
🥳🥳🥳 Thrilled to share that this NeurIPS paper was selected as 🏆 #1 Paper of the Day on Oct. 20th!
Sampled Reasoning Paths for the AIME dataset (from 1983 to 2024)
This dataset contains sampled reasoning paths for the AIME_1983_2024 dataset, released as part of the NeurIPS 2025 paper: "A Theoretical Study on Bridging Internal Probability and Self-Consistency for LLM Reasoning" (Arxiv).… See the full description on the dataset page: https://huggingface.co/datasets/WNJXYK/AIME_1983_2024-Reasoning-Paths.Medical-Reasoning-SFT-Baichuan-M3-235B
Medical-Reasoning-SFT-Baichuan-M3-235B
A large-scale medical reasoning dataset generated using baichuan-inc/Baichuan-M3-235B, containing over 124,000 samples with detailed chain-of-thought reasoning for medical and healthcare questions.
Baichuan-M3-235B is ranked #1 on HealthBench Total leaderboard and achieves state-of-the-art performance on medical reasoning benchmarks.
Dataset Overview
Metric
Value
Model
baichuan-inc/Baichuan-M3-235B
Total Samples
124… See the full description on the dataset page: https://huggingface.co/datasets/OpenMed/Medical-Reasoning-SFT-Baichuan-M3-235B.Fino1_Reasoning_Path_FinQA
Fino1 Reasoning Paths (FinQA)
📄 Paper · 🤗 Collection · 💻 Code · 🏆 Leaderboard · 🌐 The Fin AI
Fino1 Reasoning Paths (FinQA) is the reasoning-path dataset used to train Fino1-8B: FinQA questions paired with GPT-4o-generated chain-of-thought. It accompanies Fino1: On the Transferability of Reasoning-Enhanced LLMs and Reinforcement Learning to Finance (arXiv:2502.08127).
Used to train: Fino1-8B.
Quick Start
from datasets import load_dataset
ds =… See the full description on the dataset page: https://huggingface.co/datasets/TheFinAI/Fino1_Reasoning_Path_FinQA.II-Medical-Reasoning-SFT
II-Medical-Reasoning-SFT
II-Medical SFT is a curated dataset designed to support the supervised fine-tuning of large language models (LLMs) for medical reasoning tasks. It comprises multi-turn dialogues, clinical case scenarios, and question-answer pairs that reflect the complex reasoning processes encountered in real-world clinical practice.
The dataset is intended to help models develop key competencies such as differential diagnosis, evidence-based decision-making, patient… See the full description on the dataset page: https://huggingface.co/datasets/Intelligent-Internet/II-Medical-Reasoning-SFT.hermes_reasoning_tool_use
TL;DR
51 004 ShareGPT conversations that teach LLMs when, how and whether to call tools.Built with the Nous Research Atropos RL stack in Atropos using a custom MultiTurnToolCallingEnv, and aligned with BFCL v3 evaluation scenarios.Released by @interstellarninja under Apache-2.0.
1 Dataset Highlights
Count
Split
Scenarios covered
Size
51 004
train
single-turn · multi-turn · multi-step · relevance
392 MB
Each row: OpenAI-style conversations… See the full description on the dataset page: https://huggingface.co/datasets/interstellarninja/hermes_reasoning_tool_use.gsm-hard
Dataset Summary
This is the harder version of gsm8k math reasoning dataset (https://huggingface.co/datasets/gsm8k).
We construct this dataset by replacing the numbers in the questions of GSM8K with larger numbers that are less common.
Supported Tasks and Leaderboards
This dataset is used to evaluate math reasoning
Languages
English - Numbers
Dataset Structure
dataset = load_dataset("reasoning-machines/gsm-hard")
DatasetDict({
train: Dataset({… See the full description on the dataset page: https://huggingface.co/datasets/reasoning-machines/gsm-hard.natural_reasoningNaturalReasoning 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.Audio-Reasoner-CoTAreasoningverifiable-code-reasoning
Verifiable Code Reasoning
Execution-verified Python problems with chain-of-thought
Sandbox-checked solutions · Multi-test unit checks · Deduplicated instances · Training-ready sft_text
Overview
Verifiable Code Reasoning is a large-scale dataset of Python coding problems where every kept solution has passed sandboxed unit tests.
Unlike scraped contest dumps or unverified LLM traces, an example enters this release only if:
a reference… See the full description on the dataset page: https://huggingface.co/datasets/smshahbaj/verifiable-code-reasoning.