datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
agent-think-tool_use
Agent Think Tool Use
Датасет многошаговых агентных сессий для дообучения моделей работе с кодом, инструментами и инженерными задачами. Записи содержат пользовательские требования, комментарии агента во время работы, decision summaries, вызовы инструментов, результаты запусков, обработку ошибок и финальную проверку.
Каждый shard представляет отдельную связанную сессию, а не отдельный вопрос и ответ. Данные охватывают исследование задачи, работу с документацией, проектирование… See the full description on the dataset page: https://huggingface.co/datasets/ru-dataset/agent-think-tool_use.thinking-benchmark-90
Thinking Benchmark
A calibration pool of 90 competition-mathematics problems assembled to study how output / reasoning-trace length varies with problem difficulty across frontier language models. Part of the Cost of Overthinking research project.
Dataset at a glance
Source
n
Difficulty
Contamination risk
AIME 2026
29
3–5
low
OlymMATH
41
4–6
medium
HMMT February 2026
12
4–5
low
MATH-500
5
2–3
high
FrontierMath-style
3
6
medium
Difficulty is… See the full description on the dataset page: https://huggingface.co/datasets/tyrtleli/thinking-benchmark-90.arxiv-qa-thinking
ArXiv Q&A with Thinking Dataset
This dataset contains question-answer pairs generated by MiniMax-M2.1 based on academic articles from PursuitOfDataScience/arxiv-llama4-maverick-abstract.
Dataset Description
For each academic article, the model generates:
Thinking process: The model's reasoning wrapped in <think> tags
Question: An insightful question testing understanding of key concepts
Answer: A detailed answer based on the article content
Statistics… See the full description on the dataset page: https://huggingface.co/datasets/PursuitOfDataScience/arxiv-qa-thinking.Chinese-Qwen3-235B-Thinking-2507-Distill-100k
📌 Note: The English translation of this dataset card is provided below.
Chinese-Qwen3-235B-Thinking-2507-Distill-100k
Dataset Summary
Chinese-Qwen3-235B-Thinking-2507-Distill-100k 是一个包含约 100k 条高质量中文推理与指令数据的数据集，由 Qwen-3-235B-A22B-Thinking-2507（官方 Thinking 模式，上下文长度 32K）蒸馏生成。
该数据集覆盖了多个重要领域：
数学与工程任务（Mathematics, Applied Math, Advanced Math）
通用知识与写作（General Knowledge, Language & Writing）
技术与编程（Technology & Programming）
商业与经济（Business & Economics）… See the full description on the dataset page: https://huggingface.co/datasets/Jackrong/Chinese-Qwen3-235B-Thinking-2507-Distill-100k.gsm8k-thinking
GSM8K Thinking
This dataset contains responses generated by MiniMax-M2.1 for math word problems from the openai/gsm8k dataset.
Dataset Description
The dataset captures both the extended thinking process and final answers from MiniMax-M2.1, with reasoning wrapped in <think> tags for easy separation.
Metric
Value
Train Examples
7,473
Test Examples
1,319
Total Examples
8,792
Total Tokens
10,506,774
Avg Tokens/Example
1,195
Source Dataset… See the full description on the dataset page: https://huggingface.co/datasets/PursuitOfDataScience/gsm8k-thinking.ppo_ppl_thinkfinal_stage10
PPO Stage-10 Curriculum Dataset
本仓库提供基于 Atomheart-Father/ppo_pool_24000_ppl10_sys10_thinkfinal_toklen 的分阶段 PPO/DPO 训练数据。源数据来自 OpenR1 子集与 OT-114k 数学子集，保持 <think>...</think><final>...</final> 的答案格式，并用 query-only PPL 做难度分桶。
数据切分
stage0 … stage9：共 10 个训练阶段。每阶段目标 2000 条（脚本参数 STAGE_SIZE=2000，FIXED_RATIO=0.8），约 80% 来自同难度分桶（stage_role=fixed），20% 为其他难度的混合样本（stage_role=random）。
eval：从剩余样本中采样（脚本参数 EVAL_SIZE=500），stage_role=eval。
test：剩余部分，stage_role=test。
stage_id：0–9 对应阶段，-1 表示… See the full description on the dataset page: https://huggingface.co/datasets/Atomheart-Father/ppo_ppl_thinkfinal_stage10.toucan-agentic-thinking
Toucan Agentic with Thinking Dataset
This dataset contains agentic reasoning responses generated by MiniMax-M2.1 based on questions from Agent-Ark/Toucan-1.5M_SFT.
Dataset Description
For each user question, the model generates:
Thinking process: The model's reasoning wrapped in <think> tags
Response: A complete, helpful answer in natural language
The original tool definitions are preserved in the tools field for reference.
Statistics
Split
Examples… See the full description on the dataset page: https://huggingface.co/datasets/PursuitOfDataScience/toucan-agentic-thinking.thinking-benchmark-hard-but-doable
Thinking Benchmark — Hard-but-Doable Panel
Eight competition-math problems selected for the Cost of Overthinking study's controlled trace-length comparison. These are the "hold the problem constant" panel: problems that every tested frontier model (GPT-5, GPT-5.4, o3) solves reliably (≥7/8 at k=8) but still has to genuinely reason about (no instant one-shots).
The goal is to observe how mean and variance of reasoning-trace length differ across models on identical, non-trivial… See the full description on the dataset page: https://huggingface.co/datasets/tyrtleli/thinking-benchmark-hard-but-doable.thinking-benchmark-gpt5-2
Thinking Benchmark — GPT-5.2 k=32 subset
Five problems selected for the k=32 deep pass of the Cost of Overthinking study, targeting GPT-5.2's capability edge (25–75% success rate on the k=8 calibration pass).
Subset of tyrtleli/thinking-benchmark-90.
Problems
id
source
domain
difficulty
k=8 rate
answer
olymmath_0552
OlymMATH
algebra
5
2/8
\frac{10057600}{3}
hmmt_2026_feb_comb_08
HMMT
combinatorics
5
2/8
6992
olymmath_0518
OlymMATH
number_theory
4… See the full description on the dataset page: https://huggingface.co/datasets/tyrtleli/thinking-benchmark-gpt5-2.clean_multilingual_thinking元データ: https://huggingface.co/datasets/HuggingFaceH4/Multilingual-Thinking
使用したコード: https://github.com/LLMTeamAkiyama/0-data_prepare/tree/master/src/Multilingual-Thinking
データ件数: 197
平均トークン数: 872
最大トークン数: 2,339
合計トークン数: 171,812
ファイル形式: JSONL
ファイル分割数: 1
合計ファイルサイズ: 748.5 KB
加工内容：
フィルタリングによるデータクレンジング
言語フィルタリング: reasoning_languageが「English」のデータのみを抽出します。
文字数フィルタリング: 処理速度の観点から、question（質問）、thought（思考）、answer（回答）の各フィールドで、規定の文字数を超える長大なデータは事前に除外します。
繰り返し表現の除去:… See the full description on the dataset page: https://huggingface.co/datasets/LLMTeamAkiyama/clean_multilingual_thinking.Think_and_Query_value_for_R1
Introduction
This repository implements a Shapley value-based approach to quantitatively evaluate the contributions of query (q) and think (t) in generating answer (a).
Method
think_value = [loss(a|q) - loss(a|q,t) + loss(a|∅) - loss(a|t)] / 2
query_value = [loss(a|t) - loss(a|q,t) + loss(a|∅) - loss(a|q)] / 2
think_ratio = think_value/loss(a|∅)
query_ratio = query_value/loss(a|∅)
Original dataset… See the full description on the dataset page: https://huggingface.co/datasets/caihuaiguang/Think_and_Query_value_for_R1.toucan-agentic-thinking
Toucan Agentic with Thinking Dataset
This dataset contains agentic reasoning responses generated by MiniMax-M2.1 based on questions from Agent-Ark/Toucan-1.5M_SFT.
Dataset Description
For each user question, the model generates:
Thinking process: The model's reasoning wrapped in <think> tags
Response: A complete, helpful answer in natural language
The original tool definitions are preserved in the tools field for reference.
Statistics
Split
Examples… See the full description on the dataset page: https://huggingface.co/datasets/agent-data/toucan-agentic-thinking.