Datasets:
Store parquet in 1,000-row groups so the dataset viewer can search and filter (same rows)
9452930 verified | 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. | |