---
license: apache-2.0
language:
- en
- zh
- es
- fr
- ar
- ru
task_categories:
- text-generation
tags:
- math
- reasoning
- multilingual
- multilingual-reasoning
- chain-of-thought
- supervised-fine-tuning
- competition-math
size_categories:
- 10K<n<100K
source_datasets:
- open-r1/OpenR1-Math-220k
configs:
- config_name: default
  data_files:
  - split: train
    path: train.parquet
  - split: test
    path: test.parquet
---

<div align="center">

# SFT-Math-Multilingual-43k

**Long chain-of-thought math solutions, each written end to end in one of six languages**

<a href="https://arxiv.org/abs/2609.37104"><img alt="arXiv: 2609.37104" src="https://img.shields.io/badge/arXiv-2609.37104-b31b1b"></a> <img alt="traces: 43,218" src="https://img.shields.io/badge/traces-43,218-eb6834"> <img alt="languages: 6" src="https://img.shields.io/badge/languages-6-2a78d6"> <img alt="license: Apache 2.0" src="https://img.shields.io/badge/license-Apache_2.0-1baf7a">

</div>

<img src="https://huggingface.co/datasets/96kevinli29/SFT-Math-Multilingual-43k/resolve/main/assets/sft_effect_4b.png" alt="Bar chart per language of the share of responses reasoning in the requested language, before and after fine-tuning: English 100% to 100%, Chinese 100% to 100%, Spanish 0% to 100%, French 0% to 99%, Arabic 0% to 98%, Russian 99% to 100%" width="100%">

<p align="center"><sub>Share of responses whose reasoning is in the requested language, Qwen3-4B before and after fine-tuning on this dataset</sub></p>

Released reasoning models often think in English whatever language the user writes in. This dataset is **multilingual supervision for the reasoning itself**: in every example the problem, the full `<think>` trace and the final solution share one language.

- **Teach a model to reason in the user's language.** Fine-tuning Qwen3-4B on it moves target-language reasoning from 39.8% to **99.4%** across Chinese, Spanish, French, Arabic and Russian.
- **Warm-start multilingual RL.** It is the SFT stage before RL on [DAPO-Math-Multilingual-6Lang](https://huggingface.co/datasets/96kevinli29/DAPO-Math-Multilingual-6Lang).
- **Study multilingual reasoning.** One source distribution, six languages, every problem in exactly one of them.

## At a glance

| | |
| :--- | :--- |
| Examples | **43,218** train · 240 test (held-out loss, 40 per language) |
| Languages | English 7,460 · Chinese 7,214 · Spanish 7,212 · French 7,146 · Arabic 6,976 · Russian 7,210 |
| Length | median 12,038 characters per solution, reasoning included |
| Source | [OpenR1-Math-220k](https://huggingface.co/datasets/open-r1/OpenR1-Math-220k), translated by Qwen3-14B and filtered |
| Trained on it | [Qwen3-4B-SFT-Math-Multilingual](https://huggingface.co/96kevinli29/Qwen3-4B-SFT-Math-Multilingual) |
| Sibling | [SFT-Math-Multilingual-56k](https://huggingface.co/datasets/96kevinli29/SFT-Math-Multilingual-56k), a separately built 56K set used at 8B |

## Quick start

```python
from datasets import load_dataset

ds = load_dataset("96kevinli29/SFT-Math-Multilingual-43k", split="train")
ex = ds[0]
print(ex["language"])                 # one of en, zh, es, fr, ar, ru
print(ex["messages"][0]["content"])   # the problem
print(ex["messages"][1]["content"])   # <think> … </think> + solution ending in \boxed{…}
```

Fields: `messages` (user problem, assistant solution), `language`, `data_source`, `category`.

<details>
<summary><b>How it was built</b></summary>

1. **Source.** English long chain-of-thought traces from [OpenR1-Math-220k](https://huggingface.co/datasets/open-r1/OpenR1-Math-220k).
2. **Assign.** Each trace gets one of the six languages in round-robin order, so every problem appears in exactly one language.
3. **Translate.** English traces stay as they are. The rest are translated chunk by chunk by a locally served Qwen3-14B; formulas and figures are detached first and reattached afterwards, and `<think>` tags are kept.
4. **Filter.** A trace is dropped if a chunk was truncated or the `\boxed` answer changed, then by seven gates: one `<think>` block, a recoverable boxed answer, balanced `$` and braces, a bounded length ratio, no repetition, and target-language fidelity.

</details>

<details>
<summary><b>Quality audit</b></summary>

| | Coherence (1–5) | Target-language fidelity (1–5) | Reaches the boxed answer | Math preserved | No major English residue |
| :--- | :---: | :---: | :---: | :---: | :---: |
| Chinese | 4.90 | 4.62 | 99.6% | 100.0% | 96.0% |
| Spanish | 4.78 | 4.40 | 99.6% | 100.0% | 94.0% |
| French | 4.81 | 4.32 | 99.6% | 100.0% | 95.6% |
| Arabic | 4.47 | 3.54 | 99.6% | 99.6% | 84.0% |
| Russian | 4.76 | 4.40 | 100.0% | 98.8% | 94.4% |
| English, untranslated | 4.88 | n/a | 100.0% | 100.0% | n/a |

250 sampled traces per language from the 43K corpus, scored by an independent judge.

</details>

## Limitations

- **Machine-translated.** A trace can contain a stray word in English or another language; Arabic is the weakest language in the audit.
- **Answers are not verified.** Translation only checks that the boxed answer is unchanged from the source.
- **Fine-tuning side effects.** Models trained on it lose some accuracy and loop on about a fifth of non-English prompts until an RL stage removes the looping; see [Qwen3-4B-SFT-Math-Multilingual](https://huggingface.co/96kevinli29/Qwen3-4B-SFT-Math-Multilingual).

## Citation

```bibtex
@article{li2026posttraining,
  title   = {What Does Post-Training Change in Multilingual Reasoning?},
  author  = {Li, Hongyang and Li, Xiao and Wu, Caesar and Danoy, Gr{\'e}goire and Bouvry, Pascal},
  journal = {arXiv preprint arXiv:2609.37104},
  year    = {2026}
}

@misc{sft-math-multilingual-43k-2026,
  title        = {{SFT-Math-Multilingual-43k}: long chain-of-thought math solutions in six languages},
  author       = {Li, Hongyang and Li, Xiao and Wu, Caesar and Danoy, Gr{\'e}goire and Bouvry, Pascal and {Sea-Fill Community}},
  year         = {2026},
  publisher    = {Hugging Face},
  howpublished = {\url{https://huggingface.co/datasets/96kevinli29/SFT-Math-Multilingual-43k}},
  note         = {Maintained by Sea-Fill Community.}
}
```

Built and released by the authors together with the [Sea-Fill Community](https://huggingface.co/SeaFill2025), which maintains this release.

Please also cite [OpenR1-Math-220k](https://huggingface.co/datasets/open-r1/OpenR1-Math-220k), the source of the English traces.