datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
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.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.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.Zebra-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-v1-20m
We are excited to release a synthetic reasoning dataset containing 22mil+ general reasoning questions and responses generated using deepseek-ai/DeepSeek-R1-Distill-Llama-70B. While there have been multiple efforts to build open reasoning datasets for math and code tasks, we noticed a lack of large datasets containing reasoning traces for diverse non code/math topics like social and natural sciences, education, creative writing and general conversations, which is why we decided to release this… See the full description on the dataset page: https://huggingface.co/datasets/glaiveai/reasoning-v1-20m.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.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.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.NLR-Causal-Reasoning
SEA Causal Reasoning
SEA Causal Reasoning evaluates a model's ability to choose the correct cause or effect given a premise. It is sampled from XCOPA for Indonesian, Tamil, Thai, and Vietnamese.
Supported Tasks and Leaderboards
SEA Causal Reasoning is designed for evaluating chat or instruction-tuned large language models (LLMs). It is part of the SEA-HELM leaderboard from AI Singapore.
Languages
Indonesian (id)
Tamil (ta)
Thai (th)
Vietnamese (vi)… See the full description on the dataset page: https://huggingface.co/datasets/aisingapore/NLR-Causal-Reasoning.TranNhiem-Vietnamese-ImageText-Reasoning
TranNhiem Vietnamese Image-Text Reasoning (V-LAION)
Large-scale Vietnamese multimodal reasoning: multi-turn visual question–answering grounded on
natural images, where every answer ships with an explicit chain-of-thought. Reasoning traces
and Answer were synthesized by Qwen3.5-397B-A17B over images from the LAION-derived Vi-Laion-gemini-VQA set.
Curated by: Trần Nhiệm Mình rất welcome cho các hợp tác liên quan tới building Data Engine và Model Training at Scale. Contact… See the full description on the dataset page: https://huggingface.co/datasets/minhnguyent546/TranNhiem-Vietnamese-ImageText-Reasoning.Fino1_Reasoning_Path_FinQAFino1 is a financial reasoning dataset based on FinQA, with GPT-4o-generated reasoning paths to enhance structured financial question answering.
For more details, please check our paper arxiv.org/abs/2502.08127.
Source Data
Initial Data Collection and Normalization
The dataset originates from FinQA dataset.
Annotations
Annotation Process
We add a prompt and create a reasoning process using GPT-4o for each question-answer pair.
💡 Citation… See the full description on the dataset page: https://huggingface.co/datasets/TheFinAI/Fino1_Reasoning_Path_FinQA.Superior-Reasoning-SFT-gpt-oss-120b-Logprob
Superior-Reasoning-SFT-gpt-oss-120b-Logprob
🚀 Overview
This dataset contains the token-level log-probabilities generated by the teacher model (gpt-oss-120b) for the reasoning samples in the main Superior-Reasoning-SFT-gpt-oss-120b Dataset.
🔗 Relationship to Main Dataset
This dataset is a companion to the main Superior-Reasoning-SFT-gpt-oss-120bdataset. Records are linked via a unique sample_uuid.
Main Dataset: Contains the text (prompts… See the full description on the dataset page: https://huggingface.co/datasets/Alibaba-Apsara/Superior-Reasoning-SFT-gpt-oss-120b-Logprob.ww2-temporal-reasoning
WWII Temporal Reasoning
A very large synthetic question-answering dataset of calendar arithmetic over World War II events: how many days or years separate two events, what weekday a date fell on, which of several events came first, how long a campaign ran, whether a claimed date or ordering is correct, and dozens of related question shapes -- plus the reverse lookup, what happened on a given date.
Every answer is computed by code, not written freehand. The event names and their… See the full description on the dataset page: https://huggingface.co/datasets/wayneworkman2012/ww2-temporal-reasoning.SWE-Bench-Verified-O1-native-tool-calling-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 using their native tool calling capabilities on the SWE-Bench Verified dataset, achieving a 45.8% success rate across 500 test instances.
Overview
This dataset was generated using the CodeAct framework, which aims to improve code… See the full description on the dataset page: https://huggingface.co/datasets/AlexCuadron/SWE-Bench-Verified-O1-native-tool-calling-reasoning-high-results.Medical-Reasoning-SFT-Mega
Medical-Reasoning-SFT-Mega
The ultimate medical reasoning dataset - combining 7 state-of-the-art AI models with fair distribution deduplication. 1.79 million unique samples with 3.78 billion tokens of medical chain-of-thought reasoning.
Dataset Overview
Metric
Value
Total Samples
1,789,998 (after deduplication)
Total Tokens
~3.78 Billion
Content Tokens
~2.22 Billion
Reasoning Tokens
~1.56 Billion
Samples with Reasoning
1,789,764 (100.0%)
Unique… See the full description on the dataset page: https://huggingface.co/datasets/OpenMed/Medical-Reasoning-SFT-Mega.staginglsat-reasoning
LSAT Reasoning
A cleaned dataset of about 14,000 LSAT-style multiple-choice questions covering the two logic-based LSAT sections: Logical Reasoning and Analytical Reasoning (Logic Games).
Most rows include a written explanation. Answer choices are normalized to a canonical one-choice-per-line format, and each row includes a chat-formatted messages field for supervised fine-tuning (SFT).
Splits
Split
Rows
train
12,497
validation
1,501
The… See the full description on the dataset page: https://huggingface.co/datasets/ooakdata/lsat-reasoning.Superior-Reasoning-SFT-gpt-oss-120b-Logprob
Superior-Reasoning-SFT-gpt-oss-120b-Logprob
🚀 Overview
This dataset contains the token-level log-probabilities generated by the teacher model (gpt-oss-120b) for the reasoning samples in the main Superior-Reasoning-SFT-gpt-oss-120b Dataset.
🔗 Relationship to Main Dataset
This dataset is a companion to the main Superior-Reasoning-SFT-gpt-oss-120b dataset. Records are linked via a unique sample_uuid.
Main… See the full description on the dataset page: https://huggingface.co/datasets/erenyeager-1/Superior-Reasoning-SFT-gpt-oss-120b-Logprob.procedural-pile
Task gallery ·
Source ·
Paper ·
RLVR dataset
Procedural Pile is a synthetic corpus of verifiable reasoning problems generated by Reasoning Core. It is intended for continued pretraining, mid-training, and supervised fine-tuning.
Answers come from procedural generators and task-specific solvers or checkers, rather than language-model generation. The corpus spans mathematics, formal logic, planning, graphs, parsing, code, structured data, and other symbolic domains. Difficulty… See the full description on the dataset page: https://huggingface.co/datasets/reasoning-core/procedural-pile.reasoning-v1-20m-portugueseglaiveai/reasoning-v1-20m translated to portuguese.
severity_ablation_logicjudged_science_completionsseverity_ablation_scienceverifiable-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.Magpie-Reasoning-V1-150K
Project Web: https://magpie-align.github.io/
Arxiv Technical Report: https://arxiv.org/abs/2406.08464
Codes: https://github.com/magpie-align/magpie
Abstract
Click Here
High-quality instruction data is critical for aligning large language models (LLMs). Although some models, such as Llama-3-Instruct, have open weights, their alignment data remain private, which hinders the democratization of AI. High human labor costs and a limited, predefined scope for prompting prevent… See the full description on the dataset page: https://huggingface.co/datasets/Magpie-Align/Magpie-Reasoning-V1-150K.general-reasoning-ift-pairs
Reasoning-IFT Pairs (General Domain)
This dataset provides the largest set of IFT and Reasoning answers pairs for a set of general domain queries (cf: math-domain).It is based on the Infinity-Instruct dataset, an extensive and high-quality collection of instruction fine-tuning data.
We curated 900k queries from the 7M_core subset of Infinity-Instruct, which covers multiple domains including general knowledge, commonsense Q&A, coding, and math.For each query… See the full description on the dataset page: https://huggingface.co/datasets/Scale-or-Reason/general-reasoning-ift-pairs.chempile-reasoning
ChemPile-Reasoning
A comprehensive collection of reasoning tasks for chemistry, spectral analysis, and scientific understanding
📋 Dataset Summary
ChemPile-Reasoning is a dataset designed for reasoning tasks in the field of chemistry. It is part of the ChemPile project, which aims to create a comprehensive collection of chemistry-related data for training language models. This dataset includes a variety of reasoning tasks derived from scientific Stack Exchange… See the full description on the dataset page: https://huggingface.co/datasets/jablonkagroup/chempile-reasoning.tiny-aya-l2-thinker-multilingual-reasoning
Tiny Aya L2 Multilingual Reasoning (44 languages)
Translated multilingual reasoning traces used to train Tiny Aya L2-Thinker.
Each example has the prompt, thinking, and answer in the same non-English language alongside the original texts in English.
Data source
Prompts from AM-DeepSeek-R1-0528-Distilled
Thinking traces and outputs distilled from gpt-oss-120b
Translated with command-a-translate and DeepSeek-V3
Languages (44)
Language
Train… See the full description on the dataset page: https://huggingface.co/datasets/CohereLabs/tiny-aya-l2-thinker-multilingual-reasoning.Maze