When Models Reason in Your Language: Controlling Thinking Trace Language
  Comes at the Cost of Accuracy
Recent Large Reasoning Models (LRMs) with thinking traces have shown strong
performance on English reasoning tasks. However, their ability to think in
other languages is less studied. This capability is as important as answer
accuracy for real world applications because users may find the reasoning trace
useful for oversight only when it is expressed in their own language. We
comprehensively evaluate two leading families of LRMs on our XReasoning
benchmark and find that even the most advanced models often revert to English
or produce fragmented reasoning in other languages, revealing a substantial gap
in multilingual reasoning. Prompt based interventions that force models to
reason in the users language improve readability and oversight but reduce
answer accuracy, exposing an important trade off. We further show that targeted
post training on just 100 examples mitigates this mismatch, though some
accuracy loss remains. Our results highlight the limited multilingual reasoning
capabilities of current LRMs and outline directions for future work. Code and
data are available at https://github.com/Betswish/mCoT-XReasoning.
683987754a3a71a917cc50ef
Jirui Qi
683987754a3a71a917cc50f0
Shan Chen
claimed_verified
2025-06-05T07:28:42.599Z
63600b93d9e4214b9a67807c
https://cdn-avatars.huggingface.co/v1/production/uploads/63600b93d9e4214b9a67807c/x_VE45ZPoaUFiryYl_5J5.png
Shan Chen
user
shanchen
683987754a3a71a917cc50f1
Zidi Xiong
683987754a3a71a917cc50f2
Raquel Fernández
683987754a3a71a917cc50f3
Danielle S. Bitterman
683987754a3a71a917cc50f4
Arianna Bisazza
When Models Reason in Your Language: Controlling Thinking Trace Language
  Comes at the Cost of Accuracy
Recent Large Reasoning Models (LRMs) with thinking traces have shown strong
performance on English reasoning tasks. However, their ability to think in
other languages is less studied. This capability is as important as answer
accuracy for real world applications because users may find the reasoning trace
useful for oversight only when it is expressed in their own language. We
comprehensively evaluate two leading families of LRMs on our XReasoning
benchmark and find that even the most advanced models often revert to English
or produce fragmented reasoning in other languages, revealing a substantial gap
in multilingual reasoning. Prompt based interventions that force models to
reason in the users language improve readability and oversight but reduce
answer accuracy, exposing an important trade off. We further show that targeted
post training on just 100 examples mitigates this mismatch, though some
accuracy loss remains. Our results highlight the limited multilingual reasoning
capabilities of current LRMs and outline directions for future work. Code and
data are available at https://github.com/Betswish/mCoT-XReasoning.
Evaluation of Large Reasoning Models in multilingual reasoning shows limited capability, with interventions improving readability but reducing accuracy.
Large Reasoning Models XReasoning benchmark multilingual reasoning prompt-based interventions post-training