Self-Improving Multilingual Long Reasoning via Translation-Reasoning Integrated Training
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.
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Junxiao Liu
claimed_verified
2026-02-06T18:51:11.681Z
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https://cdn-avatars.huggingface.co/v1/production/uploads/68356f5db243fb809813a715/grhHvANfDRp75rMJxWlQo.jpeg
LiuJunxiao
master-lan
user
master-lan
6985662b4ad556f294b7ebf9
Zhijun Wang
6985662b4ad556f294b7ebfa
Yixiao Li
6985662b4ad556f294b7ebfb
Zhejian Lai
claimed_verified
2026-02-09T08:35:21.194Z
643525ea0b30bd434ea15363
https://cdn-avatars.huggingface.co/v1/production/uploads/643525ea0b30bd434ea15363/7sAzllfWUPtt68NY1gDLj.png
Jackie Lai
DreamW1ngs
user
DreamW1ngs
6985662b4ad556f294b7ebfc
Liqian Huang
6985662b4ad556f294b7ebfd
Xin Huang
6985662b4ad556f294b7ebfe
Xue Han
6985662b4ad556f294b7ebff
Junlan Feng
6985662b4ad556f294b7ec00
Shujian Huang
Self-Improving Multilingual Long Reasoning via Translation-Reasoning Integrated Training
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.
TRIT framework improves multilingual reasoning by jointly training translation and reasoning components, enhancing question understanding and response generation across languages.
multilingual reasoning translation reasoning integrated training cross-lingual question alignment COMET FLORES-200 MMATH