Pushing on Multilingual Reasoning Models with Language-Mixed
  Chain-of-Thought
Recent frontier models employ long chain-of-thought reasoning to explore
solution spaces in context and achieve stonger performance. While many works
study distillation to build smaller yet capable models, most focus on English
and little is known about language-specific reasoning. To bridge this gap, we
first introduct **Language-Mixed CoT**, a reasoning schema that switches
between English and a target language, using English as an anchor to excel in
reasoning while minimizing translation artificats. As a Korean case study, we
curate **Yi-Sang**: 5.79M native-Korean prompts from web Q&A, exams, STEM, and
code; 3.7M long reasoning traces generated from Qwen3-32B; and a targeted 260k
high-yield subset. We train ninve models (4B-35B) across six families (Qwen2.5,
Llama-3.1, Gemma-3, etc). Our best model, **KO-REAson-35B**, achieves
state-of-the-art performance, with the highest overall average score (64.0 \pm
25), ranking first on 5/9 benchmarks and second on the remainder. Samller and
mid-sized models also benefit substantially, with an average improvement of
+18.6 points across teh evaluated nine benchmarks. Ablations show
**Language-Mixed CoT** is more effective than monolingual CoT, also resulting
in cross-lingual and mult-modal performance gains. We release our data-curation
pipeline, evaluation system, datasets, and models to advance research on
language-specific reasoning. Data and model collection:
https://huggingface.co/KOREAson.
68e7a5f744180c42eca8afa7
Guijin Son
68e7a5f744180c42eca8afa8
Donghun Yang
68e7a5f744180c42eca8afa9
Hitesh Laxmichand Patel
68e7a5f744180c42eca8afaa
Amit Agarwal
68e7a5f744180c42eca8afab
Hyunwoo Ko
claimed_verified
2025-10-10T09:13:34.356Z
63e087b6a98d931aa90c1b9c
https://cdn-avatars.huggingface.co/v1/production/uploads/63e087b6a98d931aa90c1b9c/96c6IT3f1pWGLbRdRDB2U.png
Hyunwoo Ko
user
Cartinoe5930
68e7a5f744180c42eca8afac
Chanuk Lim
68e7a5f744180c42eca8afad
Srikant Panda
68e7a5f744180c42eca8afae
Minhyuk Kim
68e7a5f744180c42eca8afaf
Nikunj Drolia
68e7a5f744180c42eca8afb0
Dasol Choi
claimed_verified
2025-10-10T09:13:43.834Z
66120647cac232c1507e13da
/avatars/f09d2218c9cfd4d74fcd95693cc0a3c2.svg
DasolChoi
user
Dasool
68e7a5f744180c42eca8afb1
Kyong-Ha Lee
68e7a5f744180c42eca8afb2
Youngjae Yu
Pushing on Multilingual Reasoning Models with Language-Mixed
  Chain-of-Thought
Recent frontier models employ long chain-of-thought reasoning to explore
solution spaces in context and achieve stonger performance. While many works
study distillation to build smaller yet capable models, most focus on English
and little is known about language-specific reasoning. To bridge this gap, we
first introduct **Language-Mixed CoT**, a reasoning schema that switches
between English and a target language, using English as an anchor to excel in
reasoning while minimizing translation artificats. As a Korean case study, we
curate **Yi-Sang**: 5.79M native-Korean prompts from web Q&A, exams, STEM, and
code; 3.7M long reasoning traces generated from Qwen3-32B; and a targeted 260k
high-yield subset. We train ninve models (4B-35B) across six families (Qwen2.5,
Llama-3.1, Gemma-3, etc). Our best model, **KO-REAson-35B**, achieves
state-of-the-art performance, with the highest overall average score (64.0 \pm
25), ranking first on 5/9 benchmarks and second on the remainder. Samller and
mid-sized models also benefit substantially, with an average improvement of
+18.6 points across teh evaluated nine benchmarks. Ablations show
**Language-Mixed CoT** is more effective than monolingual CoT, also resulting
in cross-lingual and mult-modal performance gains. We release our data-curation
pipeline, evaluation system, datasets, and models to advance research on
language-specific reasoning. Data and model collection:
https://huggingface.co/KOREAson.
A language-mixed chain-of-thought reasoning approach improves performance in Korean-specific tasks by switching between English and Korean, achieving state-of-the-art results across various benchmarks.
chain-of-thought reasoning Language-Mixed CoT monolingual CoT Yi-Sang Qwen3-32B KO-REAson-35B cross-lingual mult-modal