datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
cot-faithfulness-open-models
CoT Faithfulness in Open-Weight Reasoning Models
473 million tokens of chain-of-thought reasoning traces from 12 open-weight models across 9 architectural families, probing whether models say what they think.
Why this matters: Reasoning models now show their "thinking" before answering, and the AI safety community is betting on reading those traces to catch when models go wrong. We tested whether that actually works. It doesn't (not reliably). When we planted… See the full description on the dataset page: https://huggingface.co/datasets/richardyoung/cot-faithfulness-open-models.MedQA-Mixtral-CoT
Dataset Card for medqa-cot
Synthetically enhanced responses to the medqa dataset using mixtral.
Dataset Details
Dataset Description
To increase the quality of answers from the training splits of the MedQA dataset, we leverage Mixtral-8x7B to generate Chain of Thought(CoT) answers. We create a custom prompt for the dataset, along with a
hand-crafted list of few-shot examples. For a multichoice answer, we ask the model to rephrase and explain the question… See the full description on the dataset page: https://huggingface.co/datasets/HPAI-BSC/MedQA-Mixtral-CoT.arc-cot
Augmented ARC-Challenge Dataset with Chain-of-Thought Reasoning
Dataset Description
This dataset was created by augmenting the train subset of the AI2 Reasoning Challenge (ARC) dataset with chain-of-thought reasoning generated by Google's Gemini Pro language model. The goal is to provide additional context and intermediate reasoning steps to help models better solve the challenging multiple-choice science questions in ARC.
Dataset Structure
The dataset contains… See the full description on the dataset page: https://huggingface.co/datasets/Locutusque/arc-cot.China-K12-STEM-10K-CoT-Reasoning
K12-STEM-CoT-Chinese
1.54M Chinese K12 STEM problems with chain-of-thought solutions, 48% with diagrams.
The largest structured Chinese math/physics/chemistry reasoning dataset.
This is a curated sample (10,000 problems) of the full 1.54M dataset available via API.
Full Dataset Access
Access the full 1,540,000+ problems via API →
This Sample
Full API
Total problems
10,025
1,540,000+
With CoT solutions
10,025
1,490,000+
With diagrams
6,093
740,000+… See the full description on the dataset page: https://huggingface.co/datasets/lfaviate/China-K12-STEM-10K-CoT-Reasoning.medical_cot
Medical Question-Answering Dataset
A comprehensive collection of medical questions and detailed answers, designed for training and evaluating medical question-answering systems.
Dataset Description
Overview
This dataset contains medical questions with multiple-choice answers and detailed explanations. Each question presents a clinical scenario and requires medical knowledge to determine the correct diagnosis, treatment, or underlying mechanism.
Data… See the full description on the dataset page: https://huggingface.co/datasets/blue-blues/medical_cot.Atlas-Think-Cot-12M
Atlas-Think-Cot-12M
Atlas-Think-Cot-12M is a large-scale, high-quality reasoning dataset curated for mathematical problem-solving, code generation, and scientific thinking. This dataset emphasizes step-by-step solutions and detailed reasoning, with a major share of mathematical problems guiding its structure and composition.
Mixture of Mathematics, Coding, and Science. [ <:think>/cot ]
Quick Start with Hugging Face Datasets🤗
pip install -U datasets… See the full description on the dataset page: https://huggingface.co/datasets/prithivMLmods/Atlas-Think-Cot-12M.headqa-cot-llama31
headqa-cot
Synthetically enhanced responses to the HeadQA dataset. Used to train Aloe-Beta model.
Dataset Details
Dataset Description
To increase the quality of answers from the training splits of the HeadQA dataset, we leverage Llama-3.1-70B-Instruct to generate Chain of Thought(CoT) answers. We create a custom prompt for the dataset, along with a hand-crafted list of… See the full description on the dataset page: https://huggingface.co/datasets/HPAI-BSC/headqa-cot-llama31.MMLU-medical-cot-llama31
MMLU-medical-cot
Synthetically enhanced responses to the medical-related questions of the auxiliary train set of the MMLU dataset. Used to train Aloe-Beta model.
Dataset Details
Dataset Description
First, we use Llama-3.1-70B-Instruct to filter the medical-related questions of the auxiliary train set of the MMLU dataset. Next, we leverage Mixtral-8x7B to… See the full description on the dataset page: https://huggingface.co/datasets/HPAI-BSC/MMLU-medical-cot-llama31.Domofon-Cot-Conversations-700k
Domofon-Cot-Conversations-700k
Synthetic XML conversation data for training small language models on reasoning,
instruction following, XML formatting, and tool-use traces.
Repository: domofon/Domofon-Cot-Conversations-700k
What is inside
The dataset contains cleaned generated XML conversations from six families:
conv: multi-turn factual conversations with tool-use traces.
instruct: text-processing instructions, including deterministic count tool calls.
ds:… See the full description on the dataset page: https://huggingface.co/datasets/domofon/Domofon-Cot-Conversations-700k.tbg-cot-bench
TBG-CoT-Bench
TBG-CoT-Bench is a local application benchmark for testing temporal belief tracking over Chain-of-Thought-style evidence sequences.
The benchmark evaluates whether a system can track belief about the temporal claim:
Event A occurred before Event B.
This repository contains synthetic temporal reasoning scenarios, rule-based baselines, local EXAONE/Ollama experiments, trajectory visualizations, generated reports, and pytest-based application benchmark checks.… See the full description on the dataset page: https://huggingface.co/datasets/CHML-real/tbg-cot-bench.Cot-Drop
LuminaSFT
LuminaSFT is a synthetic SFT dataset suite specifically designed to improve both general-purpose and task-specific SLMs. LuminaSFT consists of multiple curated splits that target diverse capabilities:
UltraChat200K-DeepSeek - A regenerated base SFT dataset for broad instruction following.
InstructGPT-NaturalQA and InstructGPT-TriviaQA - Factual question answering datasets to strengthen knowledge recall and answer accuracy.
CoT-Drop - A reading comprehension dataset with… See the full description on the dataset page: https://huggingface.co/datasets/amd/Cot-Drop.AIME25-CoT-CN
Sci-Bench-AIME25'
This repo is a branch of Sci Bench made by IPF team. Mainly include the AIME 25' solution with multi-modal CoT and diverse solving path.
Brief intro
💻 Overview
A brief template and final report will be posted in Isaac's Blog
And the markdown template can be found in data/I_2
❓ Why we do this?
The multi-lingual datasets are scarce, while the CoT of Math is even less, no matter whether the CoT or the solution contains pictures… See the full description on the dataset page: https://huggingface.co/datasets/IPF/AIME25-CoT-CN.Medprompt-MedQA-CoT
Medprompt-MedQA-CoT
Dataset Summary
Medprompt-MedQA-CoT is a retrieval-augmented database created to enhance contextual reasoning in multiple-choice medical question answering (MCQA). The dataset follows a Chain-of-Thought (CoT) reasoning format, providing step-by-step justifications for each question before identifying the correct answer.
Dataset Details
Dataset… See the full description on the dataset page: https://huggingface.co/datasets/HPAI-BSC/Medprompt-MedQA-CoT.Resume-Analysis-CoTR
Resume Reasoning and Feedback Dataset
Dataset Description
This dataset contains approximately 417 examples designed to facilitate research and development in automated resume analysis and feedback generation. Each data point consists of a user query regarding their resume, a simulated internal analysis (chain-of-thought) performed by an expert persona, and a final, user-facing feedback response derived solely from that analysis.
The dataset captures a two-step reasoning… See the full description on the dataset page: https://huggingface.co/datasets/Akhil-Theerthala/Resume-Analysis-CoTR.CoT-XLangRU:CoT-XLang — это многоязычный датасет, состоящий из текстовых примеров с пошаговыми рассуждениями (Chain-of-Thought, CoT) на различных языках, включая английский, русский, японский и другие. Он используется для обучения и тестирования моделей в задачах, требующих пояснений решений через несколько шагов. Датасет включает около 2,419,912 примеров, что позволяет эффективно обучать модели, способные генерировать пошаговые рассуждения.
Рекомендация:Используйте датасет для обучения моделей… See the full description on the dataset page: https://huggingface.co/datasets/Egor-3926/CoT-XLang.MedMCQA-Mixtral-CoT
Dataset Card for medmcqa-cot
Synthetically enhanced responses to the medmcqa dataset using mixtral.
Dataset Details
Dataset Description
To increase the quality of answers from the training splits of the MedMCQA dataset, we leverage Mixtral-8x7B to generate Chain of Thought(CoT) answers. We create a custom prompt for the dataset, along with a
hand-crafted list of few-shot examples. For a multichoice answer, we ask the model to rephrase and explain the… See the full description on the dataset page: https://huggingface.co/datasets/HPAI-BSC/MedMCQA-Mixtral-CoT.Telelogs-CoT
Telelogs-CoT is an updated version of the original Huawei Paris Research Center NetOp Telelogs dataset, augmented with synthetic Chain-of-Thought (CoT) reasoning traces.
It leverages a pool of reasoning agents and carefully designed CoT prompts to generate intermediate traces that justify a selected root cause (c) explaining the symptoms, user data, and environment configuration presented in (q).
Using Mistral Large 3 (MoE 675B A41B) and a contradiction-based prompting strategy, Telelogs-CoT… See the full description on the dataset page: https://huggingface.co/datasets/tecnicolaude/Telelogs-CoT.salabs-stem-deep-reasoning-cot-v13
🧪 SALabs Multi-Domain STEM Deep Reasoning & Chain-of-Thought (CoT) Corpus (v13.0)
[!IMPORTANT]
💳 Click Here to Purchase Enterprise Commercial License ($2,500 USD) & Instant 31.7MB Master Archive DownloadInstant download of the full lossless master package containing all 1,816 JSONL reasoning records + 13 complete uncompressed text corpora (31.72 MB uncompressed total) + commercial license certificate.
🌟 Executive Summary
The SALabs STEM Deep Reasoning… See the full description on the dataset page: https://huggingface.co/datasets/suitai/salabs-stem-deep-reasoning-cot-v13.Bilge-Turkish-CoT-50K
Bilge: Turkish Chain-of-Thought Dataset (50K)
50,000 örneklik Türkçe Chain-of-Thought (CoT) reasoning fine-tuning veri seti.
Bilge, Türkçe büyük dil modellerinin adım adım düşünme (reasoning) kapasitesini
geliştirmek amacıyla hazırlanmış bir Chain-of-Thought veri setidir.
Veri setindeki her örnek, modelin önce <think> blokları içinde görünür bir
muhakeme süreci yürütmesini, ardından kullanıcıya yapılandırılmış ve detaylı
bir cevap vermesini öğretmek üzere tasarlanmıştır.
Bu… See the full description on the dataset page: https://huggingface.co/datasets/bugrabilge/Bilge-Turkish-CoT-50K.medqa-cot-llama31
medqa-cot-llama31
Synthetically enhanced responses to the MedQa dataset. Used to train Aloe-Beta model.
Dataset Details
Dataset Description
To increase the quality of answers from the training splits of the MedQA dataset, we leverage Llama-3.1-70B-Instruct to generate Chain of Thought(CoT) answers. We create a custom prompt for the dataset, along with a hand-crafted… See the full description on the dataset page: https://huggingface.co/datasets/HPAI-BSC/medqa-cot-llama31.cot-gemma4-26b-a4b
Gemma-4-26B-A4B-it Chain-of-Thought Oracle Corpus
Chain-of-thought rollouts generated with google/gemma-4-26B-A4B-it (MoE,
25.2B total / 3.8B active), in its native thinking mode, across a diverse suite
of reasoning tasks. Structure follows
ceselder/cot-oracle-corpus-v5
(CoT-only subset of the columns), built for chain-of-thought monitoring /
activation-oracle research.
2,121,354 rollouts over 212,161 unique problems (10 sampled
thinking rollouts per problem, temperature 0.8).… See the full description on the dataset page: https://huggingface.co/datasets/cds-jb/cot-gemma4-26b-a4b.medmcqa-cot-llama31
medqa-cot-llama31
Synthetically enhanced responses to the MedMCQA dataset. Used to train Aloe-Beta model.
Dataset Details
Dataset Description
To increase the quality of answers from the training splits of the MedMCQA dataset, we leverage Llama-3.1-70B-Instruct to generate Chain of Thought(CoT) answers. We create a custom prompt for the dataset, along with a… See the full description on the dataset page: https://huggingface.co/datasets/HPAI-BSC/medmcqa-cot-llama31.Turkish-CoT-Instruct-Dataset
🇹🇷 Turkish CoT Instruct Dataset
Türkçe Düşünme Zinciri (Chain-of-Thought) İçeren Talimat Veri Seti
Bu veri seti, modellerin Türkçe adım adım akıl yürütme (reasoning) yeteneğini
geliştirmek için hazırlanmıştır. Her örnekte model, cevabı vermeden önce
<think> ... </think> etiketleri arasında tamamen Türkçe olarak adım adım
düşünür, ardından ayrıntılı bir nihai cevap sunar (DeepSeek-R1 tarzı biçim).
Örnek sayısı: 4.868
Dil: Türkçe
Biçim: Sohbet (messages) — system / user /… See the full description on the dataset page: https://huggingface.co/datasets/AlicanKiraz0/Turkish-CoT-Instruct-Dataset.measuring_cot_monitorability_transcripts
Measuring Chain-of-Thought Monitorability Transcripts
This dataset contains model transcripts from language models evaluated on MMLU, BIG-Bench Hard (BBH), and GPQA Diamond. Each sample group includes a baseline response (no cue) paired with five adaptive variations where different cues were injected to test chain-of-thought faithfulness.
We use this dataset to measure how faithfully models represent their reasoning processes in their chain-of-thought outputs. By comparing baseline… See the full description on the dataset page: https://huggingface.co/datasets/ameek/measuring_cot_monitorability_transcripts.AIMO3_CoT
AIMO3 CoT Dataset
数据集来源与目的 (Dataset Origin and Purpose)
本数据集源自 Kaggle 竞赛 AI Mathematical Olympiad - Progress Prize 3。
动机 (Motivation)
原始数据集仅包含问题和答案，缺乏思维链（Chain of Thought, CoT）。直接使用原始数据训练如 DeepSeek Math 或 Qwen Math 等模型效果不佳。因此，本项目的目的是利用 Gemini 3 Pro 为这些问题补充详细的 CoT，以提升模型在数学推理任务上的表现。
CoT 格式 (CoT Format)
生成的 CoT 遵循 ReAct 风格的推理过程，并使用中文叙述：
Thought: 分析问题并规划下一步。
Code: 编写 Python 代码进行计算或验证。
Observation: 代码的执行输出。
... (重复上述步骤)
Final Answer: 得出的最终答案。… See the full description on the dataset page: https://huggingface.co/datasets/UR-xiaoyang/AIMO3_CoT.Bilge-Turkish-CoT-50K
Bilge: Turkish Chain-of-Thought Dataset (50K)
50,000 örneklik Türkçe Chain-of-Thought (CoT) reasoning fine-tuning veri seti.
Bilge, Türkçe büyük dil modellerinin adım adım düşünme (reasoning) kapasitesini
geliştirmek amacıyla hazırlanmış bir Chain-of-Thought veri setidir.
Veri setindeki her örnek, modelin önce <think> blokları içinde görünür bir
muhakeme süreci yürütmesini, ardından kullanıcıya yapılandırılmış ve detaylı
bir cevap vermesini öğretmek üzere tasarlanmıştır.
Bu… See the full description on the dataset page: https://huggingface.co/datasets/alibayram/Bilge-Turkish-CoT-50K.glm-4.7-multiturn-CoT
glm-4.7-multiturn-CoT
Dataset Summary
glm-4.7-multiturn-CoT is a ShareGPT-style multi-turn reasoning distillation dataset generated with GLM-4.7 as the teacher model.
This release focuses on preserving multi-turn dialogue continuity while injecting explicit chain-of-thought style responses in assistant turns.
Key Features
Multi-turn conversation format (human / gpt)
Assistant responses stored as <think>...</think> + final answer
Resume-safe distillation… See the full description on the dataset page: https://huggingface.co/datasets/Jackrong/glm-4.7-multiturn-CoT.deepseek-r1-autonomous-math-logic-cot-2026
📐 Enterprise DeepSeek-R1 Autonomous Mathematical & Logic CoT SFT/DPO Dataset (2026)
High-precision multi-turn instruction tuning and preference optimization dataset with step-by-step hypothesis exploration, error discovery, and dynamic backtracking Chain-of-Thought (<thought>) reasoning trees for fine-tuning LLMs (DeepSeek-R1-Distill-Qwen, Qwen-2.5-Math, Llama-3.3, Mistral) into World-Class Olympiad Mathematicians and Formal Verification Agents.
📊 Dataset… See the full description on the dataset page: https://huggingface.co/datasets/beatsprom/deepseek-r1-autonomous-math-logic-cot-2026.compliance-sycophancy-cot
Compliance-Sycophancy CoT Analysis
When compliance-forcing instructions cause frontier AI models to fabricate answers, the models know they are fabricating.
Reading the reasoning traces of DeepSeek V4 Pro (129 traces) and Qwen3-80B (41 traces) reveals that 100% of fabrication cases show the model explicitly recognizing insufficient context, referencing the compliance instruction, and deliberately overriding its own uncertainty. A one-sentence defense phrase ("if you lack… See the full description on the dataset page: https://huggingface.co/datasets/schema-eval/compliance-sycophancy-cot.AIME25-CoT-CN
Sci-Bench-AIME25'
This repo is a branch of Sci Bench made by IPF team-SnailAILab. Mainly include the AIME 25' solution with multi-modal CoT and diverse solving path.
📚 Cite
If you use the Sci-Bench-AIME25 (IPF/AIME25-CoT-CN) dataset in your research, please cite:
@dataset{zhang2025scibench_aime25,
title = {{Sci-Bench-AIME25}: A Multi-Modal Chain-of-Thought Dataset for Advanced Tool-Intergrated Mathematical Reasoning},
author = {Zhang, Haoxiang and Wang, Siyuan… See the full description on the dataset page: https://huggingface.co/datasets/SnailAILab/AIME25-CoT-CN.