---
title: Logics-STEM-SFT-Dataset-Open-1.6M
canonical_url: "https://www.modelscope.cn/datasets/Alibaba-DT/Logics-STEM-SFT-Dataset-Open-1.6M"
md_url: "https://www.modelscope.cn/datasets/Alibaba-DT/Logics-STEM-SFT-Dataset-Open-1.6M.md"
repository: Alibaba-DT/Logics-STEM-SFT-Dataset-Open-1.6M
last_updated: 2026-01-19
license: cc-by-nc-4.0
storage_size: "60 GB"
downloads: 1188
stars: 2
---

# Logics-STEM-SFT-Dataset-Open-1.6M

> Logics-STEM-SFT-Dataset-Open-1.6M - Alibaba-DT 在 ModelScope 开源的数据集。Logics-STEM-SFT-Dataset-2.2M

Alibaba-DT/Logics-STEM-SFT-Dataset-Open-1.6M 是 ModelScope 魔搭社区上的数据集，存储大小 60 GB，采用 cc-by-nc-4.0 许可。

- **Repository**: Alibaba-DT/Logics-STEM-SFT-Dataset-Open-1.6M
- **License**: cc-by-nc-4.0
- **Storage size**: 60 GB
- **Downloads**: 1188
- **Stars**: 2
- **Last updated**: 2026-01-19

Source: https://www.modelscope.cn/datasets/Alibaba-DT/Logics-STEM-SFT-Dataset-Open-1.6M

---

# Logics-STEM-SFT-Dataset-2.2M

## 📰 News
- [2026.01.05]🔥 Release of our [Techinical Report](https://arxiv.org/abs/2601.01562).
- [2026.01.05]🔥 Release the first version of [Logics-STEM-8B-SFT](https://huggingface.co/Logics-MLLM/Logics-STEM-8B-SFT), [Logics-STEM-8B-RL](https://huggingface.co/Logics-MLLM/Logics-STEM-8B-RL), [/Logics-STEM-SFT-Dataset-Open-1.6M](https://huggingface.co/datasets/Logics-MLLM/Logics-STEM-SFT-Dataset-Open-1.6M).

---

# Overview
### What is this dataset?

**Logics-STEM-SFT-Dataset-2.2M** is a curated **long Chain-of-Thought (CoT) SFT dataset for STEM reasoning**, built on top of high-quality open-source data and enhanced through a rigorous curation and distillation data engine. It consists of prompt–response pairs distilled by **Qwen3-235B-A22B-Thinking-2507**, covering Math and broader STEM domains (e.g., physics, chemistry, biology, engineering, and computer science). The dataset is designed to serve as a strong and general-purpose SFT baseline for training reasoning-capable LLMs.

Due to licensing constraints, some internal components (e.g., **DLR-book**) cannot be fully open-sourced. As a result, we open **Logics-STEM-SFT-Dataset-Open-1.6M**, derived from the reported 2.2M downsampled version while preserving the same curation principles and data format.
### How is it curated?
![](./imgs/long_cot_data_engine.png)

We adopt a data curation engine with the following stages:

1. **Annotation**: validity/unambiguity filtering; discipline/domain; educational level; answer type; verifiable answer (when applicable)
2. **Deduplication**: exact + near-duplicate removal
3. **Decontamination**: removal of samples overlapping with evaluation benchmarks (MinHash + n-gram)
4. **Response distillation**: teacher-model long-CoT generation; repetition suppression; optional verification & regeneration
5. **Weighted stratified sampling**: uses **response length as a proxy for difficulty**, balancing hard reasoning density and broad coverage


## Experimental Results

![](./imgs/math_evaluation_res.png)
![](./imgs/stem_evaluation_res.png)

## Quickstart

### Load with `datasets`

```python
from datasets import load_dataset

ds = load_dataset("Logics-MLLM/Logics-STEM-SFT-Dataset-2.2M")
print(ds)
print(ds["train"][0])
```

## Data Collection
We collect questions from the following publicly available datasets:

- **NuminaMath-1.5** (AI-MO/NuminaMath-1.5)  
- **OpenThoughts3** (OpenThoughts)  
- **Mixture-of-Thoughts / Open-R1** (Hugging Face Open-R1 related data recipe)  
- **AceReason-1.1-SFT** (AceReason-Nemotron 1.1 SFT data)  
- **AceReason-Math**  
- **OpenScienceReasoning-2** (nvidia/OpenScienceReasoning-2)  
- **OpenMathReasoning** (OpenMathReasoning; AIMO-2 winning solution dataset)  
- **Llama-Nemotron-Post-Training-Dataset** (nvidia Llama-Nemotron post-training dataset)  
- **DLR-Web** (DESIGNER: web-derived synthetic set)  
- **DLR-Book** (DESIGNER: book-derived synthetic set)  
- **Skywork-OR1-RL-Data** (Skywork Open Reasoner RL data)  
- **NaturalReasoning** (NaturalReasoning)  
- **DeepMath-103K** (DeepMath-103K)  
- **DAPO-Math-17K** (DAPO Math 17K)  
- **TheoremQA** (TheoremQA)  
- **JEEBench** (JEEBench)  
- **GPQA-Main** (GPQA)  
- **GSM8K** (GSM8K)  
- **AIME** (di-zhang-fdu/AIME_1983_2024)  
- **AMC** (kaggle-aimo/amc_filtered)  
- **s1-teasers** (s1 dataset)  
- **s1-probs** (s1 dataset)  
- **openaimath** (s1 / OpenAI-math style dataset referenced by s1)

*Acknowledgement: We sincerely thank the creators and maintainers of these open-source datasets for making this work possible.*

## Citation
If you use this dataset, please cite our technical report:

```bibtex
@misc{xu2026logicsstemempoweringllmreasoning,
      title={Logics-STEM: Empowering LLM Reasoning via Failure-Driven Post-Training and Document Knowledge Enhancement}, 
      author={Mingyu Xu and Cheng Fang and Keyue Jiang and Yuqian Zheng and Yanghua Xiao and Baojian Zhou and Qifang Zhao and Suhang Zheng and Xiuwen Zhu and Jiyang Tang and Yongchi Zhao and Yijia Luo and Zhiqi Bai and Yuchi Xu and Wenbo Su and Wei Wang and Bing Zhao and Lin Qu and Xiaoxiao Xu},
      year={2026},
      eprint={2601.01562},
      archivePrefix={arXiv},
      primaryClass={cs.AI},
      url={https://arxiv.org/abs/2601.01562}, 
}
```