datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
deepscaler-preview-verl
DeepScaleR-Preview VERL
📊 Dataset Summary
This dataset contains 35,789 mathematical reasoning problems in VERL format, processed from agentica-org/DeepScaleR-Preview-Dataset.
Key Features:
35,789 high-quality math problems
Converted to VERL format for reward modeling
Verified ground truth answers
Ready for reinforcement learning training
🔗 Source Dataset
Original Repository
Repository:… See the full description on the dataset page: https://huggingface.co/datasets/sungyub/deepscaler-preview-verl.DeepScaleR-EasyThe easy part (pass_rate > 4/8) of DeepScaleR
Check https://wutaiqiang.github.io/project/Art for more details
Citation:
@inproceedings{wu2026art,
title={The Art of Efficient Reasoning: Data, Reward, and Optimization},
author={Taiqiang Wu and Zenan Xu and Bo Zhou and Ngai Wong},
year={2026},
url={https://arxiv.org/pdf/2602.20945}
}
deepscaler
deepscaler
DeepScaler math problems - challenging mathematical reasoning problems.
Dataset Structure
This dataset is in Hugging Face datasets format. Load it with:
from datasets import load_dataset
dataset = load_dataset("Tyrion279/deepscaler")
DeepScale-qwen2.5_7b-multi使用Qwen2.5-7b-Instruct模型，从DeepScaler数据集中抽取20000条数据，使用多轮对话的方式获得数学答案。
prompt模板：
使用的system_prompt是LLAMA_MATH_SYSTEM_PROMPT
多轮对话的prompt是ITER_GEN_MULTI_TURN_STEP_PROMPTS
LLAMA_MATH_SYSTEM_PROMPT = """
Solve the following math problem efficiently and clearly:
- For simple problems (2 steps or fewer):
Provide a concise solution with minimal explanation.
- For complex problems (3 steps or more):
Use this step-by-step format:
## Step 1: [Concise description]
[Brief explanation and calculations]
##… See the full description on the dataset page: https://huggingface.co/datasets/tttonyyy/DeepScale-qwen2.5_7b-multi.DeepScaleR-Preview-Dataset-Japanese
DeepScaleR Preview Dataset (Japanese Translation)
agentica-org/DeepScaleR-Preview-Dataset の日本語翻訳版です。
データセットの説明
数学の問題、解答、解法を含むデータセットです。元のデータセットを日本語に翻訳しました。
使用方法
from datasets import load_dataset
ds = load_dataset("kfsky/DeepScaleR-Preview-Dataset-Japanese")
print(ds["train"][0])
カラム
カラム名
説明
problem
数学の問題文（日本語）
answer
解答
solution
解法の詳細（日本語）
unit
数学の単元名
ライセンス
元データセットと同じ MIT License に従います。
謝辞
元データセット:… See the full description on the dataset page: https://huggingface.co/datasets/kfsky/DeepScaleR-Preview-Dataset-Japanese.