Crosslingual Reasoning through Test-Time Scaling
Reasoning capabilities of large language models are primarily studied for
English, even when pretrained models are multilingual. In this work, we
investigate to what extent English reasoning finetuning with long
chain-of-thoughts (CoTs) can generalize across languages. First, we find that
scaling up inference compute for English-centric reasoning language models
(RLMs) improves multilingual mathematical reasoning across many languages
including low-resource languages, to an extent where they outperform models
twice their size. Second, we reveal that while English-centric RLM's CoTs are
naturally predominantly English, they consistently follow a quote-and-think
pattern to reason about quoted non-English inputs. Third, we discover an
effective strategy to control the language of long CoT reasoning, and we
observe that models reason better and more efficiently in high-resource
languages. Finally, we observe poor out-of-domain reasoning generalization, in
particular from STEM to cultural commonsense knowledge, even for English.
Overall, we demonstrate the potentials, study the mechanisms and outline the
limitations of crosslingual generalization of English reasoning test-time
scaling. We conclude that practitioners should let English-centric RLMs reason
in high-resource languages, while further work is needed to improve reasoning
in low-resource languages and out-of-domain contexts.
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Zheng-Xin Yong
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2025-05-09T07:20:45.630Z
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Yong Zheng-Xin
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M. Farid Adilazuarda
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Jonibek Mansurov
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2025-05-09T09:03:39.843Z
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Jonibek Mansurov
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MJonibek
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Ruochen Zhang
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Niklas Muennighoff
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2025-05-09T09:04:00.900Z
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Niklas Muennighoff
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Muennighoff
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Carsten Eickhoff
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Genta Indra Winata
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2025-05-09T09:04:13.878Z
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Genta Indra Winata
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Julia Kreutzer
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2025-05-09T09:04:29.257Z
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Julia Kreutzer
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Stephen H. Bach
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Alham Fikri Aji
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2025-05-12T06:50:58.421Z
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Alham Fikri Aji
user
afaji
Crosslingual Reasoning through Test-Time Scaling
Reasoning capabilities of large language models are primarily studied for
English, even when pretrained models are multilingual. In this work, we
investigate to what extent English reasoning finetuning with long
chain-of-thoughts (CoTs) can generalize across languages. First, we find that
scaling up inference compute for English-centric reasoning language models
(RLMs) improves multilingual mathematical reasoning across many languages
including low-resource languages, to an extent where they outperform models
twice their size. Second, we reveal that while English-centric RLM's CoTs are
naturally predominantly English, they consistently follow a quote-and-think
pattern to reason about quoted non-English inputs. Third, we discover an
effective strategy to control the language of long CoT reasoning, and we
observe that models reason better and more efficiently in high-resource
languages. Finally, we observe poor out-of-domain reasoning generalization, in
particular from STEM to cultural commonsense knowledge, even for English.
Overall, we demonstrate the potentials, study the mechanisms and outline the
limitations of crosslingual generalization of English reasoning test-time
scaling. We conclude that practitioners should let English-centric RLMs reason
in high-resource languages, while further work is needed to improve reasoning
in low-resource languages and out-of-domain contexts.
Scaling English-centric reasoning models and controlling chain-of-thought language improves multilingual mathematical reasoning but has limitations in low-resource languages and out-of-domain contexts.
reasoning language models chain-of-thoughts English-centric multilingual long chain-of-thoughts low-resource languages high-resource languages out-of-domain reasoning cultural commonsense knowledge STEM