title: When Models Reason in Your Language: Controlling Thinking Trace Language
    Comes at the Cost of Accuracy
summary: 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.
paper_authors._id: 683987754a3a71a917cc50ef
paper_authors.name: Jirui Qi
paper_authors._id: 683987754a3a71a917cc50f0
paper_authors.name: Shan Chen
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-06-05T07:28:42.599Z
paper_authors._id: 63600b93d9e4214b9a67807c
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/63600b93d9e4214b9a67807c/BovBE1oiZPdU8Td9I2YIn.jpeg
paper_authors.fullname: Shan Chen
paper_authors.type: user
paper_authors.user: shanchen
paper_authors._id: 683987754a3a71a917cc50f1
paper_authors.name: Zidi Xiong
paper_authors._id: 683987754a3a71a917cc50f2
paper_authors.name: Raquel Fernández
paper_authors._id: 683987754a3a71a917cc50f3
paper_authors.name: Danielle S. Bitterman
paper_authors._id: 683987754a3a71a917cc50f4
paper_authors.name: Arianna Bisazza
paper_title: When Models Reason in Your Language: Controlling Thinking Trace Language
    Comes at the Cost of Accuracy
paper_summary: 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.
paper_ai_summary: Evaluation of Large Reasoning Models in multilingual reasoning shows limited capability, with interventions improving readability but reducing accuracy.
paper_ai_keywords: Large Reasoning Models XReasoning benchmark multilingual reasoning prompt-based interventions post-training