datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
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.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.kimi-cyber-reasoning
Kimi Cyber Reasoning
997 chain-of-thought records covering 13 cybersecurity disciplines and 4 systems engineering domains, distilled from the Kimi K3 reasoning model via API. Every record provides an explicit step-by-step <think> reasoning trace followed by a technical resolution, unified code diff fix, or structured tool invocation.
The dataset was curated as an anchor set for training, healing, and specializing compact reasoning models on systems security and tool calling… See the full description on the dataset page: https://huggingface.co/datasets/echel0nn1881/kimi-cyber-reasoning.Fable-5.1-Max-Reasoning-Filtered-5000x
Dataset Description
This dataset contains 5,000 agentic coding and reasoning multi-turn traces generated by the new Fable 5.1 model using max reasoning effort.
It holds almost 150,000,000 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
5,000 Traces
Total… See the full description on the dataset page: https://huggingface.co/datasets/MoreThought/Fable-5.1-Max-Reasoning-Filtered-5000x.Superior-Reasoning-SFT-gpt-oss-120b
Superior-Reasoning-SFT-gpt-oss-120b
📣 News
Our dataset ranked #1 on the Hugging Face Datasets Trending leaderboard from January 20 to January 30.
🚀 Overview
The Superior-Reasoning-SFT-gpt-oss-120b dataset is a high-quality, open-source collection containing 435K samples designed to democratize the training of high-performance Long Chain-of-Thought (Long-CoT) models. Unlike standard distilled datasets that rely on random sampling or… See the full description on the dataset page: https://huggingface.co/datasets/Alibaba-Apsara/Superior-Reasoning-SFT-gpt-oss-120b.Cybersecurity_Reasoning_Dataset
Cybersecurity Reasoning Dataset (Model-Agnostic)
A model-agnostic re-architecture of the Cybersecurity Reasoning Dataset. The original
corpus was format-bound to the Mistral/Llama ### Instruction: / ### Response: template;
this dataset losslessly separates reasoning content from format, providing one
neutral canonical corpus plus four per-family rendered training variants
(Mistral/Llama, DeepSeek, ChatML, Gemma).
Why this exists. Identical content scored 88.1 on a… See the full description on the dataset page: https://huggingface.co/datasets/dpevzner/Cybersecurity_Reasoning_Dataset.claude-opus-4.6-4.7-reasoning-8.7k
Background
Ended up with some tokens to burn on a Claude Max plan. Assembly began during 4.6 and moved to 4.7. Model is tagged. The development evolved as it went along. The dataset has not been manually reviewed. It's entirely Claude developed.
Clarification on Reasoning
The reasoning is not Claude's actual chain-of-thought (cot) and is not summarized cot. It's a fully synthetic cot created as part of the Assistant response to mimic the type of "thinking"… See the full description on the dataset page: https://huggingface.co/datasets/angrygiraffe/claude-opus-4.6-4.7-reasoning-8.7k.GLM-5.1-Reasoning-1M-Cleaned
GLM-5.1-Reasoning-1M-Cleaned
GLM-5.1-Reasoning-1M-Cleaned is a cleaned and reformatted derivative of Kassadin88/GLM-5.1-1000000x. It preserves the original four-subset layout (main, PHD-Science, Multilingual-STEM, Math) while converting every example into a unified SFT-ready schema with explicit conversations, input, output, domain, and meta fields.
This release was prepared from the original dataset published by Kassadin88.
Summary
Teacher model in the data: GLM-5.1… See the full description on the dataset page: https://huggingface.co/datasets/Jackrong/GLM-5.1-Reasoning-1M-Cleaned.Stitched-Reasoning-Trajectories-7M
Stitched-Reasoning-Trajectories-7M
Dataset Summary
Stitched-Reasoning-Trajectories-7M is a massive-scale, synthetic multi-hop reasoning dataset. It was built by algorithmically "stitching" together discrete reasoning traces from the original glaiveai/reasoning-v1-20m dataset into continuous, coherent, and logically structured multi-agent trajectories.
By extracting internal sub-questions from <think> blocks and mapping high-information keyword overlaps, this dataset… See the full description on the dataset page: https://huggingface.co/datasets/ajibawa-2023/Stitched-Reasoning-Trajectories-7M.gsm8k-multilingual-reasoning
gsm8k-multilingual-reasoning
GSM8K with reasoning translated to multiple languages
Schema
{"prompt": "...", "answer": "...", "reasoning": "...", "metadata": {...}}
Usage
from datasets importload_dataset
ds = load_dataset("eddie-OB/gsm8k-multilingual-reasoning")
print(ds["train"][0])
Source
Derived from OpenAI GSM8K.
math-reasoning-sft-100k
Math Reasoning SFT (100K)
100,000 math problems with detailed step-by-step solutions — ready for supervised fine-tuning of math reasoning models.
Dataset Description
100,000 problems across 8 mathematical categories and 3 difficulty levels:
Categories
Category
Examples
Topics
word_problems
~23,100
Rate/time/distance, work problems, mixture, meeting/catch-up
arithmetic
~15,400
Percentages, profit/loss, ratios
geometry
~15,400
Area… See the full description on the dataset page: https://huggingface.co/datasets/stindardlogic/math-reasoning-sft-100k.Kimi-K2.5-Reasoning-1M-Cleaned
🪐 Kimi-K2.5-Reasoning-1M-Cleaned
Kimi-K2.5-Reasoning-1M-Cleaned is a cleaned derivative of ianncity/KIMI-K2.5-1000000x. It preserves the original four-config layout from the source dataset and rewrites each record into a unified reasoning-SFT schema with id, conversations, input, output, domain, and meta.
Summary
Source dataset: ianncity/KIMI-K2.5-1000000x
Source author: ianncity
Teacher model recorded in meta.teacher_model: KIMI-K2.5
Token lengths computed with… See the full description on the dataset page: https://huggingface.co/datasets/Jackrong/Kimi-K2.5-Reasoning-1M-Cleaned.reasoning-base-20k
Dataset Card for Reasoning Base 20k
Dataset Details
Dataset Description
This dataset is designed to train a reasoning model. That can think through complex problems before providing a response, similar to how a human would. The dataset includes a wide range of problems from various domains (science, coding, math, etc.), each with a detailed chain of thought (COT) and the correct answer. The goal is to enable the model to learn and refine its reasoning process… See the full description on the dataset page: https://huggingface.co/datasets/KingNish/reasoning-base-20k.gemini-3.1-pro-hard-high-reasoning
Dataset Card for Gemini-3.1-Pro-Ultra-Reasoning-5.6M
Dataset Details
Dataset Description
This dataset represents the frontier of synthetic reasoning data, generated by Gemini 3.1 Pro (High Reasoning variant). While smaller in total token volume than its predecessors (5.6M tokens), this corpus prioritizes logical density and multi-step verification.
The move to the 3.1 architecture provides a measurable leap in "System 2" thinking. Unlike standard models… See the full description on the dataset page: https://huggingface.co/datasets/Roman1111111/gemini-3.1-pro-hard-high-reasoning.medical-clinical-reasoning-sft-100k
Medical Clinical Reasoning SFT 100K
A synthetic supervised fine-tuning dataset of 100,000 high-quality medical and clinical reasoning conversations designed to train AI assistants capable of supporting clinical decision-making, documentation, and medical education.
Dataset Description
This dataset covers a broad spectrum of clinical practice scenarios across 10 medical specialty categories. Each record follows the ShareGPT conversation format with a detailed human… See the full description on the dataset page: https://huggingface.co/datasets/stindardlogic/medical-clinical-reasoning-sft-100k.financial-economics-reasoning
Model Card
📌 Summary
financial-economics-reasoning dataset was constructed using advanced Inference Distillation techniques. We employed the qwen-3-235b-a22b-thinking-2507 model as the Teacher Model to process the open-source BAAI/IndustryInstruction_Finance-Economics dataset, which contains 122,378 bilingual (Chinese-English) entries in finance, economics, and business.
Unlike standard distillation datasets that only provide final answers, this dataset retains the… See the full description on the dataset page: https://huggingface.co/datasets/Jackrong/financial-economics-reasoning.MiMo-2.5-Pro-Reasoning-Traces-Hard
MiMo-2.5-Pro-Reasoning-Traces-Hard
A large-scale reasoning dataset of 8,706 expert-level prompts with full reasoning traces across 44 academic and technical topics, generated using the MiMo-v2.5-Pro model. Each entry contains the step-by-step reasoning chain alongside the final completion, designed for training and evaluating advanced reasoning capabilities in language models.
Dataset Statistics
Metric
Value
Total entries
8,706
Unique topics
44… See the full description on the dataset page: https://huggingface.co/datasets/Skyhigh-2203/MiMo-2.5-Pro-Reasoning-Traces-Hard.reasoning-math-advanced-1m
🧠 Reasoning Math Advanced 1M
📖 Dataset Summary
Reasoning Math Advanced 1M is a large-scale, synthetic dataset designed to enhance the reasoning capabilities of Large Language Models (LLMs). Comprising 1,000,000 unique samples, this dataset focuses on Math, Logic, and Common Sense reasoning tasks.
A unique feature of this dataset is its adaptive reasoning structure, where the presence of Chain-of-Thought (CoT) reasoning scales with difficulty. All reasoning traces are… See the full description on the dataset page: https://huggingface.co/datasets/naimulislam/reasoning-math-advanced-1m.claude-reasoning
Claude Reasoning Dataset
This dataset is a curated collection of prompts and responses generated by Claude by Anthropic. It combines high-quality long reasoning data from multiple sources to provide a focused training set for models requiring logic, math, and coding capabilities.
If multiple answers were generated for the same input during the data collection process, the entry with the shortest reasoning content was selected to ensure conciseness and high signal-to-noise ratio.… See the full description on the dataset page: https://huggingface.co/datasets/agentlans/claude-reasoning.gpt-oss-120b-reasoning-STEM-5K
GPT-OSS-120B-Distilled-Reasoning-STEM Dataset
1) Dataset Overview
Data Source Model: gpt-oss-120b-high
Task Type: STEM Reasoning and Problem Solving (Science, Technology, Engineering & Mathematics)
Data Format: `JSON Lines
Fields: generator, category, input, CoT_Native——reasoning, answer
(Consistent with the math dataset, splitting the original 'output' into 'reasoning' and 'answer' for COT/SFT scenarios.)
2) Design Goals (Motivation)
This dataset targets… See the full description on the dataset page: https://huggingface.co/datasets/Jackrong/gpt-oss-120b-reasoning-STEM-5K.Fable-5-Max-Reasoning-Filtered-250x
Dataset Description
This dataset contains 25. highly detailed architectural traces mapping out security implementations for hybrid global banking systems encompassing both fiat and cryptocurrency infrastructures.
This is 10,000,000+ estimated tokens of fable 5 data, filtered and classified to remove low-quality entries by qwen 2.5 7B, and improved by GLM 5.2. The dataset bypasses basic conversational filler and is engineered to advance the domain precision, strict formatting… See the full description on the dataset page: https://huggingface.co/datasets/MoreThought/Fable-5-Max-Reasoning-Filtered-250x.Fable-5.1-Max-Reasoning-Filtered-1000x
Dataset Description
This dataset contains 1,000 coding and reasoning traces generated by the new Fable 5.1 model using max reasoning effort.
It holds almost 30,000,000 tokens of step-by-step chain-of-thought programming across multiple complex domains.
It has also been deduplicated and filtered to remove low-quality traces, keeping only high-quality traces.
Dataset Statistics
Metric
Value
Total Examples
1,000 Traces
Total Token Count
~30,000,000… See the full description on the dataset page: https://huggingface.co/datasets/KrazyKitty/Fable-5.1-Max-Reasoning-Filtered-1000x.gemma3n-conversational-reasoning
Gemma3N Conversational Reasoning
This dataset is prepared for Unsloth Gemma3/Gemma3N conversational notebooks that use:
from datasets import load_dataset
from unsloth.chat_templates import standardize_data_formats
dataset = load_dataset("Cyleux/gemma3n-conversational-reasoning", split="train[:3000]")
dataset = standardize_data_formats(dataset)
Schema:
conversations: ShareGPT-style list of turns with from and value
metadata columns are included for analysis and filtering
Notes:… See the full description on the dataset page: https://huggingface.co/datasets/Cyleux/gemma3n-conversational-reasoning.llm-cipher-reasoning
llm-cipher-reasoning — data, eval results and full research ledger
Everything except the weights from a research run asking: can an LLM be trained to reason in a
more compact "language" than English, and does that actually save tokens?
Two linked lines of work on Qwen/Qwen3-4B-Instruct-2507:
Cipher invention / cross-model communication — cold-decoding tests, negotiated cipher
collusion between model pairs, a cipher-hardening arms race, and GEPA prompt optimization to get
a… See the full description on the dataset page: https://huggingface.co/datasets/AlexWortega/llm-cipher-reasoning.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.Pashto-Free-Hand-Reasoning-Dataset
Pashto Free-Hand Reasoning SFT Dataset 🧠♻️
This dataset contains high-quality, long-form SFT (Supervised Fine-Tuning) conversational data in Pashto, featuring unconstrained, natural model reasoning (<think> blocks) paired with standardized chat responses.
🔄 The 3R Approach (Recycle, Reuse, Reason)
Instead of discarding legacy QA pairs, this dataset follows a 3R data philosophy:
Recycle: Taking older, simple, or raw legacy Pashto questions.
Reuse: Re-processing… See the full description on the dataset page: https://huggingface.co/datasets/nassimjp/Pashto-Free-Hand-Reasoning-Dataset.SlimPajama-Meta-rater-Reasoning-30B
Top 30B token SlimPajama Subset selected by the Reasoning rater
This repository contains the dataset described in the paper Meta-rater: A Multi-dimensional Data Selection Method for Pre-training Language Models.
Code: https://github.com/opendatalab/Meta-rater
Dataset Description
This dataset contains the top 30B tokens from the SlimPajama-627B corpus, selected using the Reasoning dimension of the PRRC (Professionalism, Readability, Reasoning, Cleanliness) framework. Each… See the full description on the dataset page: https://huggingface.co/datasets/opendatalab/SlimPajama-Meta-rater-Reasoning-30B.social-reasoning-rlhf
Dataset Summary
This repository provides access to a social reasoning dataset that aims to provide signal to how humans navigate social situations, how they reason about them and how they understand each other. It contains questions probing people's thinking and understanding of various social situations.
This dataset was created by collating a set of questions within the following social reasoning tasks:
understanding of emotions
intent recognition
social norms
social… See the full description on the dataset page: https://huggingface.co/datasets/ProlificAI/social-reasoning-rlhf.UltraMath-Reasoning-Small
Dataset Card for UltraMath Reasoning
Dataset Details
Dataset Description
Curated by: [Reality123b]
Funded by [Reality123b]:
Shared by [Reality123b & Roman]:
Language(s) (NLP): [English, synthetic arithmatic]
License: [MIT]
Dataset Sources [optional]
Repository: [Currently only available on huggingface]->
Uses
Direct Use
[Synthetic Pretraining Corpus]
Out-of-Scope Use
[Dataset not intended to develop… See the full description on the dataset page: https://huggingface.co/datasets/DataMuncher-Labs/UltraMath-Reasoning-Small.verified-research-reasoning-trajectories
Verified Research Reasoning Trajectories for RLVR
This repository is the public sample and schema repository for Ulam's research-level mathematical reasoning trajectories for reinforcement learning with verifiable rewards (RLVR), process supervision, judge training, proof criticism, and private evaluations.
Ulam Verified Research Reasoning Trajectories are proof-process data for RLVR. Each record contains a normalized research problem, a golden or partial-golden proof graph… See the full description on the dataset page: https://huggingface.co/datasets/ulamai/verified-research-reasoning-trajectories.