toroe/Soofi-Think-SFT-10B-multilingual
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---
configs:
- config_name: default
data_files:
- split: english
path: data/english-*
- split: italian
path: data/italian-*
- split: german
path: data/german-*
- split: french
path: data/french-*
- split: spanish
path: data/spanish-*
dataset_info:
features:
- name: messages
list:
- name: content
dtype: string
- name: role
dtype: string
- name: source
dtype: string
- name: dataset_name
dtype: string
- name: ds_uid
dtype: int64
- name: language
dtype: string
- name: row_index
dtype: int64
splits:
- name: english
num_bytes: 33593217838
num_examples: 2283204
- name: italian
num_bytes: 25920841021
num_examples: 2283204
- name: german
num_bytes: 27324918834
num_examples: 2283204
- name: french
num_bytes: 29051025609
num_examples: 2283204
- name: spanish
num_bytes: 28383397334
num_examples: 2283204
download_size: 66853417655
dataset_size: 144273400636
license: apache-2.0
language:
- en
- de
- fr
- es
- it
---
# Reason<sub>XL</sub>: A Multilingual Cross-Domain Reasoning Corpus
**Reason**<sub>XL</sub> is a large-scale multilingual reasoning corpus spanning 5 languages and ~44B tokens in total. It is designed to support supervised fine-tuning of reasoning models with in-language chain-of-thought traces across diverse technical domains.
---
## Data Generation
English source samples were drawn from 10 existing reasoning datasets, filtered and quality-annotated using [`ellamind/propella-1-4b`](https://huggingface.co/ellamind/propella-1-4b), and then translated into four European languages (German, French, Spanish, Italian) using `Qwen3-32B` served via vLLM.
Each sample consists of three independently translated components: the **user input**, the **reasoning trace** (within `<think>` tags), and the **final output**. Translation used nucleus sampling at low temperature (T=0.1, top-p=1.0) with a dedicated system prompt instructing the model to preserve technical terminology, mathematical notation, and reasoning structure.
English samples were annotated across 18 properties (safety, information density, educational value, audience, domain, etc.) and filtered through a multi-stage pipeline enforcing integrity constraints, domain-dependent quality thresholds, and class-aware downsampling for domain balance. Annotations transfer directly to all translations without re-annotation.
---
### Translation Prompt
Each field (input, reasoning trace, output) was translated independently using the following prompt template:
---
```
SYSTEM: You are a professional translator specializing in technical and
educational content. Translate the following {field} text into {language}.
CRITICAL INSTRUCTIONS:
1. Output ONLY the translated text
2. Preserve ALL technical terms, code snippets, mathematical notation,
and formatting exactly
3. Maintain the same tone, style, and formality
4. {language-specific formality guidance}
5. For code: Keep variable/function names in English
6. For math: Preserve LaTeX notation unchanged
7. Adapt examples and cultural references appropriately
8. Maintain terminology consistency throughout
```
---
```
USER: TEXT TO TRANSLATE:
{text}
```
---
Language-specific formality guidance:
- **German**: Use formal German (*Sie*) for professional/technical content
- **Spanish**: Use neutral Spanish suitable for international audiences
- **French**: Use standard French with appropriate formality
- **Italian**: Use standard Italian with professional tone
---
## Data Sources
| Dataset | Config | Samples |
|---|---|---|
| Cascade-SFT-Stage-2 | general / math | 768,615 |
| Dolci-Think-SFT-7B | science | 347,453 |
| Cascade-SFT-Stage-1 | general / code / math / science | 711,812 |
| Llama-Nemotron-PTD | science | 267,147 |
| Nemotron-Science-v1 | — | 97,026 |
| Nemotron-IF-Chat-v1 | — | 91,151 |
| **Total** | | **2,282,204** |
---
## Statistics
| Language | Tokens (B) | Avg. Total Length | Avg. Input | Avg. Output |
|---|---|---|---|---|
| English (`en`) | 9.2 | 4,023 | 424 | 3,599 |
| German (`de`) | 8.8 | 3,866 | 504 | 3,363 |
| French (`fr`) | 8.8 | 3,872 | 493 | 3,379 |
| Spanish (`es`) | 8.7 | 3,796 | 478 | 3,318 |
| Italian (`it`) | 8.5 | 3,742 | 495 | 3,247 |
| **Total** | **44.07** | — | — | — |
The corpus is designed as a **living resource** — the translation pipeline is ongoing, with the full release containing approximately twice as many tokens per language as the current version.
---
## Citation
```bibtex
@misc{reasonxl2026,
title = {Reason{XL}: A Multilingual Cross-Domain Reasoning Corpus},
author = {Daniil Gurgurov and Tom Röhr},
year = {2026},
publisher = {Hugging Face},
howpublished = {\url{https://huggingface.co/datasets/toroe/Soofi-Think-SFT-10B-multilingual}}
}
```
> Paper citation will be added upon publication.
configs:
- config_name: default
data_files:
- split: english
path: data/english-*
- split: italian
path: data/italian-*
- split: german
path: data/german-*
- split: french
path: data/french-*
- split: spanish
path: data/spanish-*
dataset_info:
features:
- name: messages
list:
- name: content
dtype: string
- name: role
dtype: string
- name: source
dtype: string
- name: dataset_name
dtype: string
- name: ds_uid
dtype: int64
- name: language
dtype: string
- name: row_index
dtype: int64
splits:
- name: english
num_bytes: 33593217838
num_examples: 2283204
- name: italian
num_bytes: 25920841021
num_examples: 2283204
- name: german
num_bytes: 27324918834
num_examples: 2283204
- name: french
num_bytes: 29051025609
num_examples: 2283204
- name: spanish
num_bytes: 28383397334
num_examples: 2283204
download_size: 66853417655
dataset_size: 144273400636
license: apache-2.0
language:
- en
- de
- fr
- es
- it
# Reason<sub>XL</sub>: 多语言跨领域推理语料库
**Reason<sub>XL</sub> 是一款大规模多语言推理语料库，涵盖5种语言，总Token数约为440亿。其设计目标是支持推理模型的监督微调，依托不同技术领域内的语言原生思维链（chain-of-thought）轨迹。**
---
## 数据生成流程
英文源样本取自10个现有推理数据集，经 [`ellamind/propella-1-4b`](https://huggingface.co/ellamind/propella-1-4b) 完成过滤与质量标注后，通过基于vLLM部署的`Qwen3-32B`模型，被翻译为4种欧洲语言（德语、法语、西班牙语、意大利语）。
每个样本包含三个独立翻译的组成部分：**用户输入**、**推理轨迹（reasoning trace）**（包裹在`<think>`标签内）以及**最终输出**。翻译过程采用低温度（T=0.1，top-p=1.0）的核采样策略，并通过专用系统提示要求模型保留专业术语、数学符号与推理结构。
英文样本共标注了18项属性（安全性、信息密度、教育价值、受众群体、领域等），并通过多阶段流水线进行过滤：该流水线强制执行完整性约束、领域相关质量阈值，并针对领域平衡进行类别感知下采样。标注结果可直接迁移至所有翻译样本，无需重新标注。
---
### 翻译提示词
每个字段（输入、推理轨迹、输出）均通过以下提示模板独立完成翻译：
---
SYSTEM: 您是一名专注于技术与教育内容的专业翻译人员，请将以下{field}文本翻译为{language}。
关键指令：
1. 仅输出翻译后的文本
2. 完整保留所有专业术语、代码片段、数学符号与格式
3. 保持一致的语气、风格与正式程度
4. {language-specific formality guidance}
5. 代码相关：保留变量/函数名称为英文
6. 数学相关：完整保留LaTeX符号
7. 适配示例与文化参考
8. 全程保持术语一致性
---
USER: 待翻译文本：
{text}
---
语言特定正式度指导：
- **德语**：专业/技术内容使用正式德语（*Sie* 称谓）
- **西班牙语**：使用面向国际受众的中性西班牙语
- **法语**：使用标准法语并采用合适的正式程度
- **意大利语**：使用标准意大利语并采用专业语气
---
## 数据来源
| 数据集 | 配置 | 样本数 |
|---|---|---|
| Cascade-SFT-Stage-2 | general / math | 768,615 |
| Dolci-Think-SFT-7B | science | 347,453 |
| Cascade-SFT-Stage-1 | general / code / math / science | 711,812 |
| Llama-Nemotron-PTD | science | 267,147 |
| Nemotron-Science-v1 | — | 97,026 |
| Nemotron-IF-Chat-v1 | — | 91,151 |
| **总计** | | **2,282,204** |
---
## 统计信息
| 语言 | Token 数（十亿） | 平均总长度 | 平均输入长度 | 平均输出长度 |
|---|---|---|---|---|
| 英语（en） | 9.2 | 4,023 | 424 | 3,599 |
| 德语（de） | 8.8 | 3,866 | 504 | 3,363 |
| 法语（fr） | 8.8 | 3,872 | 493 | 3,379 |
| 西班牙语（es） | 8.7 | 3,796 | 478 | 3,318 |
| 意大利语（it） | 8.5 | 3,742 | 495 | 3,247 |
| **总计** | **44.07** | — | — | — |
该语料库被设计为**动态更新资源**，当前翻译流程仍在推进中，完整版本的各语言Token数约为当前版本的两倍。
---
## 引用格式
bibtex
@misc{reasonxl2026,
title = {Reason{XL}: A Multilingual Cross-Domain Reasoning Corpus},
author = {Daniil Gurgurov and Tom Röhr},
year = {2026},
publisher = {Hugging Face},
howpublished = {url{https://huggingface.co/datasets/toroe/Soofi-Think-SFT-10B-multilingual}}
}
> 正式出版后将补充论文引用信息。
提供机构：
toroe
