README.md

---
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: 38062379774
    num_examples: 2538450
  - name: italian
    num_bytes: 30928229955
    num_examples: 2538450
  - name: german
    num_bytes: 31882959921
    num_examples: 2538450
  - name: french
    num_bytes: 33569899978
    num_examples: 2538450
  - name: spanish
    num_bytes: 32775272029
    num_examples: 2538450
  download_size: 76345944428
  dataset_size: 167218741657

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 five languages, with 2,538,450 positionally aligned examples per language (12,692,250 rows 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
 
---

## Quality Assurance

The corpus was processed with a reproducible, rule-based QA pipeline after the
initial quality filtering. Measurement and policy were kept separate: audit
flags were retained for traceability, while only explicit failure conditions
triggered rejection.

| Stage | Checks and actions |
|---|---|
| **Normalisation and structure** | Normalised content-free leading `system` turns, then validated message roles, non-empty content, balanced and ordered `<think>` tags, a non-empty final answer, code-fence parity, boxed-answer braces, and truncation markers. |
| **Translation-leak repair** | Used a language-agnostic structural detector for leaked numbered translator-instruction blocks. A block was removed only when the remaining reasoning was substantive, the final answer remained non-empty, and tag balance was preserved; otherwise the row was rejected. |
| **Language fidelity** | Removed code, LaTeX, markup, and URLs from the text used for language measurement, then applied fastText `lid.176` at prompt, document, reasoning, final-answer, and paragraph-segment levels. A mismatch was rejected only when it could be attributed to translation failure; legitimate translation, grammar, and multilingual tasks were preserved. |
| **Degeneration and encoding** | Checked for repetition loops, all-caps prose, untranslated segments, mojibake, Unicode replacement characters, and translated prompt or label leakage. |
| **Semantic consistency** | Extracted canonical boxed, multiple-choice, or short numeric answers and compared non-English outputs with their English anchor when both sides had an extractable answer. |
| **Parallel-corpus controls** | Required unique provenance keys, intersected the five surviving key sets, and performed deterministic whole-conversation deduplication on English. Every deduplication decision was applied to all five languages together, followed by an independent full lockstep verification. |

Explicit rejection conditions covered invalid or truncated structure, malformed
markup, unrecoverable pipeline leakage, severe degeneration, attributable
wrong-language or untranslated content, and extractable final-answer mismatch.
Unattributed language-ID mismatches remained observations rather than rejection
triggers to avoid biasing the corpus against legitimate multilingual tasks.

At release time, the five-way intersection contained **2,797,825** keys.
English-reference content deduplication removed **259,375** aligned groups,
leaving **2,538,450** examples per language with zero duplicate join keys. The
independent verifier checked all positions across all five splits, and every
published Parquet shard was checked for schema, compression, row count, and
cross-language key order.

---

## Data Sources

| Dataset | Config | Samples |
|---|---|---|
| Cascade-SFT-Stage-2 | general / math | 714,589 |
| Dolci-Think-SFT-7B | science | 510,466 |
| Cascade-SFT-Stage-1 | general / code / math / science | 689,603 |
| Llama-Nemotron-PTD | science | 484,703 |
| Nemotron-Science-v1 | — | 100,419 |
| Nemotron-IF-Chat-v1 | — | 38,670 |
| **Total** | | **2,538,450** |

---

## Release Statistics

| Language | Split | Examples | Parquet shards | Download size |
|---|---|---:|---:|---:|
| English | `english` | 2,538,450 | 76 | 17.71 GB |
| German | `german` | 2,538,450 | 62 | 14.64 GB |
| French | `french` | 2,538,450 | 66 | 14.97 GB |
| Spanish | `spanish` | 2,538,450 | 64 | 14.76 GB |
| Italian | `italian` | 2,538,450 | 58 | 14.26 GB |
| **Total** | — | **12,692,250** | **326** | **76.35 GB** |

This release combines both completed translation batches, retains only keys present in all five languages, and applies group-wise English-content deduplication so positional alignment is preserved.

---

## Data Integrity

All five splits contain the same 2,538,450 logical examples in the same order. Alignment is keyed by `dataset_name`, `ds_uid`, `source`, and `row_index`. From the 2,797,825-key five-language intersection, 259,375 duplicate English-content groups were removed together across every language. The published schema matches the original release: `messages`, `source`, `dataset_name`, `ds_uid`, `language`, and `row_index`.

---

## 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/ReasonXL-SFT}}
}
```

> Paper citation will be added upon publication.