Self-Improving Multilingual Long Reasoning via Translation-Reasoning Integrated Training
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866914308489740288 |
|---|---|
| author | Liu, Junxiao Wang, Zhijun Li, Yixiao Lai, Zhejian Huang, Liqian Huang, Xin Han, Xue Feng, Junlan Huang, Shujian |
| author_facet | Liu, Junxiao Wang, Zhijun Li, Yixiao Lai, Zhejian Huang, Liqian Huang, Xin Han, Xue Feng, Junlan Huang, Shujian |
| contents | Long reasoning models often struggle in multilingual settings: they tend to reason in English for non-English questions; when constrained to reasoning in the question language, accuracies drop substantially. The struggle is caused by the limited abilities for both multilingual question understanding and multilingual reasoning. To address both problems, we propose TRIT (Translation-Reasoning Integrated Training), a self-improving framework that integrates the training of translation into multilingual reasoning. Without external feedback or additional multilingual data, our method jointly enhances multilingual question understanding and response generation. On MMATH, our method outperforms multiple baselines by an average of 7 percentage points, improving both answer correctness and language consistency. Further analysis reveals that integrating translation training improves cross-lingual question alignment by over 10 percentage points and enhances translation quality for both mathematical questions and general-domain text, with gains up to 8.4 COMET points on FLORES-200. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_05940 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Self-Improving Multilingual Long Reasoning via Translation-Reasoning Integrated Training Liu, Junxiao Wang, Zhijun Li, Yixiao Lai, Zhejian Huang, Liqian Huang, Xin Han, Xue Feng, Junlan Huang, Shujian Computation and Language cs.CL Long reasoning models often struggle in multilingual settings: they tend to reason in English for non-English questions; when constrained to reasoning in the question language, accuracies drop substantially. The struggle is caused by the limited abilities for both multilingual question understanding and multilingual reasoning. To address both problems, we propose TRIT (Translation-Reasoning Integrated Training), a self-improving framework that integrates the training of translation into multilingual reasoning. Without external feedback or additional multilingual data, our method jointly enhances multilingual question understanding and response generation. On MMATH, our method outperforms multiple baselines by an average of 7 percentage points, improving both answer correctness and language consistency. Further analysis reveals that integrating translation training improves cross-lingual question alignment by over 10 percentage points and enhances translation quality for both mathematical questions and general-domain text, with gains up to 8.4 COMET points on FLORES-200. |
| title | Self-Improving Multilingual Long Reasoning via Translation-Reasoning Integrated Training |
| topic | Computation and Language cs.CL |
| url | https://arxiv.org/abs/2602.05940 |