Pushing on Multilingual Reasoning Models with Language-Mixed Chain-of-Thought
Authors: Guijin Son
Donghun Yang
Hitesh Laxmichand Patel
Amit Agarwal
Hyunwoo Ko
Chanuk lim
Srikant Panda
Minhyuk Kim
Nikunj drolia
Dasol Choi
Kyong-Ha Lee
Youngjae Yu
Venue: ICLR 2026 Poster (2026)
Keywords: Multilingual
Math
Reasoning
Primary area: foundation or frontier models, including LLMs

Abstract: Recent frontier models employ long-chain-of-thought reasoning to explore solution spaces in context and achieve stronger 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 introduce **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 artifacts.  As a Korean case study, we curate **Yi-Sang-HQ**: 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 nine 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 ($64.0_{\pm2.5}$), ranking first on 5/9 benchmarks and second on the remainder. Smaller and mid-sized models also benefit substantially, with an average improvement of $+18.6$ points across the evaluated nine benchmarks. Ablations show **Language-Mixed CoT** is more effective than monolingual CoT, indicating that reasoning patterns can be engineered to improve non-English performance. We release our data-curation pipeline, evaluation system, datasets, and models to advance research on language-specific reasoning.

Published as a conference paper at ICLR 2026
PUSHING ON MULTILINGUAL REASONING MODELS WITH LANGUAGE-MIXED CHAIN-OF-THOUGHT
Guijin Son 1,6
Donghun Yang"
Hitesh Laxmichand Patel
Amit Agarwal
Hyunwoo Ko
Chanuk Lim? 2
Srikant Panda
Minhyuk Kim
Nikunj Drolia
Dasol Choi
Kyong-Ha Lee
Youngjae Yu
'OneLineAl
KISTI
Oracle AI
*Korea University
AIM Intelligence
"Seoul National University
"University College Dublin
Yonsei University
spthszbvls1220yonseeii
ABSTRACT
Recent frontier models employ long-chain-of-thouge reasoning to explore solution spaces in context and achieve stronger 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 introduce Language-Mixed COT, reasoning schema that switches between English and a target language, using English as an anchor to excel reasoning while minimizing translation artifacts. As a Korean case study, 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 targeted 260k hield subset. We train nine models (4B 3BB35B) across six families (Qwen2.5, Llama-3.1, Gemma-3, etc). Our best model, KO-REAson-355 the highest overall average score (64.012.5). ranking first on 5/9 benchmarks and second the remainder. Smaller and mid-sized models also benefit substantially, with an average improvement of +18.6 points across the evaluated nine benchmarks. Ablations show Language-Mixed CoT is more effective than monolingual CoT, also resulting in pross-lingual and multi modal performance gains. We release our data-curation pipeline, evaluation system, datasets, and models advance research on language-specific reasoning
achieves mate-of-thheart performance, with
INTRODUCTION
Test-time scaling amplifies reasoning by allocating more samples or steps, enabling exploration, and self-correction (Jones) 202Tp. Recent advances show that large language models can internalize similar exploratory behavior (Gandii et al.) 2025) through longer chain-of -ft---oouhhhhhh (CoT) acquired during training. Specifically, such behaviors stem during the post-training training phase through reinforcement learning with verifiable rewards (RLVR) (OLMo et al., 2024 Guo et al. 2025). Unfortunately, such methodologies tend to be effective only for strong base models with large parameters (Yang et al.. 2025; Rastogi et al.) 2025). Therefore, open efforts have centered on distillation from frontier teacher models, combining systematic prompt collection with response generation and quality filtering (Muennighoff et al. 2025 Bercovich et al. 2025 Guha et al. 2025 Hugging Face 2025 Such pipelines, however, overwhelmingly target English and, to a lesser extent, Chinese (Liu et al. 2025a). leaving open how to achieve language-specitn reasoning. To bridge this gap, study how to construct a reasoning model for a mid-resource language through a focused case study in Korean.
We start from the empirical observation that pipelines relying heavily on translated corpora (lightblue 2025
Lee et al. 2025a) exhibit degraded response quality from translation artifacts (Park et al. et al. 2025a) and poor robustness everyday, colloquial expressions that rarely appear
2025
"Correspnnding Authors
Data and Model Collection http::hhhggiiiggffccceee

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Published as a conference paper at ICLR 2026
Input (Korean):
SEASOS CPT-OSS 203
RI
OwO
3,060 21481 A # 2178 2021E) SBN #+ 14 2
Dosp
1) 2) [...]
# 210 7178 2,580/ 24 X| WaEl /| W
Math (Ka
KMMLU-Hand
Monolingual CoT
Language-Mixed CoT
KMMLL-
<thinkk> We have 3, 3,060 residents Avg household 2.5 about 1,224 households.
<think>
2 RA X Ma]?] wee?? ++ +Ill
AIME24 (Ko)
compute in English keep the final answer Korean.
Each household 20L 40ek Total waste = 1,224 x 40 48,960L//wekk.
bags
Households 3,060/ / 25 1,224, Weekly per household # 20L 40L.
CLick
KSM
Per person per week 48,960 3,060 18L
Total = 1,224xxx =0=00:4::00 48,966 week, Iell 12/9) #5 48,960
</think>
3,060
KoBALT-700
</think>
Reasoning in English
Semantic in Korean
MAE-RAE Bench
KMMLLJ-Rodux
Figure 1: (Left) Thinking styles. Red: monolingual CoT carried out entirely in English. Blue: our proposed Language-Mixel CoT, which alternates between English (anchor) and Korean (target). (Right) Performance comparison of KO-REAson-35B (ours, solid line) with DeepSeek-R1-322 Exaone-Deep-32B, GPT-OSS- 20B, and QwQ-32B. KO-REAson-335 achieves top-tier performance, ranking first or second all tasks.
in translated text. To address this, we propose two-step approach: (i) data curation, where we collect 5.79M Korean, astr-authored Q&A prompts from the web to ensure broad coverage of natural, in-the-wild language; and (ii) reasoming supervision, where, when generating long reasoning traces with Qwen3-32B (Yang et al.) 2025), we enforce Language-Mixed CoT, which allows the model to switch freely during the Think step between an anchor language (English) and the target language (Korean). This enables the model to leverage the anchor language's 's reasoning capabilities while preserving the semantics of the target language. In our experiments. Language- Mixed CoT consistently outperforms monolingual COT, with larger gains on gasoninggheaay tasks relative to Korean-only, and on cultural understanding- heavy tasks relative to English-only.
The collected dataset, YI-SANG. comprises 5. 5.79M prompts paired with 3.7M long reasoning traces. To the best of our knowledge, this is the largest publicly documented post-training resource for the Korean language. To chart affordable path strong reasoning models, conduct ablations (some scaling to thousands of H100 GPU-hours) covering teacher models, augmentation schemes, and seed sources, and we iteratively filter patterns that produce loss spikes. This process yields a downsampled YI-SANG- HQ of 260k high-yield examples, on which we train the KO- REAson series. As shown in Table 4 KO-REAson-355
100
outperforms state-of-the- models trained on closed data (GPT-OSS-20B (Agarwal et al. 20256). R R1-Distill-32B (Guo et al., 2025). QwQ 32B (Team] 2025). MAANE-Deeep-322 (Research et al. 2025)) on average across nine tasks. We further demonstrate that these gains are consistent across model families and scales by training nine models (4B-35B) spanning six families. Finally, we observe tross-lingual and multi-modal gains, despite training only Korean text. Taken together, these results indicate that careful prompting and large-scale data collection can build open-recipes to rival closed systems.
Our contributions are summarized follows:
We introduce YI-SANG, the largest publicly documented post-training dataset for Korean to date, comprising 5.79M prompts and 3.7M long reasoning traces, plus a 260k high-yield subset (YI-SANG-HQ) distilled via extensive ablations.
We propose Language-Mixed CoT, a supervision scheme that lets models switch between anchor language (English) and the target language (Korean) during the Think step, yielding significant gains over monolingual CoTT baselines.
We train and release the KO-REAson series (4B-352 across five families) under the Apache-2.0 license, surpassing closed systems of comparable scale on nine benchmarks.
PRELIMINARIES AND RELATED WORKS
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Recent work has pushed long reasoning into the mainstream. ol (Jaech et al.] 2024) showed that extending the "thinking length' of model improves performance, while RI (Guo et al.) 2025) revealed how long reasoning traces are structured and how to build models capable of such capability. DeepSeek also demonstrated that SFT-distilles (e.g.. DeepSeek-Distille variants can inherit much of this ability from supervised fine-tuning alone. Subsequent efforts
Table I: Performance of Qwen2.5-1.5B-Insttee be- fore and after fine-tuning. Fine-tuning on translated OpenTTouugtt 1 14K for five epochs improves perfor- mance on MATH while degrading on HAE-RAE Bench.
Model
HRB
MATH
Qwen2.5-1.5B
35.24
25.48
+ +RRANSLATED OT
15.34
74.35
span online RL Yuu et al. 2025; Chen et al., 2025| Luo et al. 2025), offline RL (Research et al.
2025; Wen et al.) 2025), ) and pure SFT (Muennighofi et al. 2025; Guha et al. 2025). A consistent pattern emerges: successful online RL from a cold start typically requires (i) a strong base model (often 230B, with solid math/coding priors) (Yang et al. [225 Rastogi al. 2025), (ii) a reliable
process or reward model (Liu et al. 2025c He et al. 2025). and (iii) large-scale, eale, high-quality data
(e.g., Numina-Math (LI et al. 2024)). These requirements increase cost and brittleness, concentrating progress in high-resource languages such as English and Chinese.
Much less is known about bootstrapping reasoning models in mid-resource languages. Directly repli- cating high-resource pipelines is often infeasible due to weaker hase models and limited high-quality data. Prior works focus on leveraging carefully designed SFT mixtures or learning objectives bring
non-English representations closer to English (Zhu et al. 2024: ?: Agarwal et al. 2025a Lai & Nissim
2024. Chen et al.
2024) or explore cross-lingual transfer either by English training (Yong et al. [025
Ranaldi & Pucci
2025 or small-scale translated datasets (Son et al. 2025 Pipatanakul et al. (025).
Following such, we first train Qwen2.5- 1.5BB Instruct on translated data from TpenThoughti (Guha
al.] 2025). As shown in Table I this model achieves improved performance on MATH but suffers substantial drop on HAE-RAE Bench (HRB) (Son et al. 2023). a Korean culture benchmark. This gap motivates to develop reliable and practical recipe for building reasoning model that attains robust performance across diverse domains, rather than focusing only on mathematical reasoning.
Yi-Sang
Our work differs from previous works by going beyond translation. We collect native prompts, systematically curate for quality, and introduce Language-Mixed CoT as a more effective supervision signal. By varying only supervi- sion format (long vs. short; language-mixed vs. monolingual), we isolate supervision ef- fects from optimization confounds and pro- vide mid-size models a stable path to long- reasoning behavior without RL. We validate this methodology in Korean, an apt testbed: a mid- resource language with active LLM research ecosystem, scratch-trained base models (Bak)
Long-CoT
Short-CoT
Tokens (Billions)
Pre PreTraining
KOREANWEEBEET
YI-Sang-HO HQ
Korcan Textbooks
10a
KoMagpic
Open Toought
Smal-areantz HAE RAE-COT-1.5M
Open-RR-Ka-SFT
10-1
2o-teasoning-25200
10-1
10'
Instances (Millions)
Figure 2: An overview of publicly available Korean datasets. YI- SANG larger than any fine-tuning dataset or pretraining corpus, with 6.77B tokens.
al. 2025 Lab 2025| KISTI 2024), dedicated general -nowlal- (Son et al. 2024 Hong et al.)
2025) and reasoning benchmarks (Ko et al. (025). and sufficiently large web corpora for data construc- tion. The proposed dataset, YI-SANG, is not only the largest Korean post-training corpus (Figure 2 but also methodological contribution: pipeline for converting noisy internet prompts into high- quality supervision. Our empirical results demonstrate its effectiveness and offer reproducible path for mid-resource communities build competitive reasoning models.
EXPERIMENTAL SETUP
3.1 TRAINING DETAILS
Models To ensure robustness in our ablations, we run experiments on two base models: Gemma-3- 4B (Team al. 2025) and Kanana-1.5-82 (Bak et al. 2025). After determining the high-yield subset, we evaluate its efficacy by training across a broader set of models, including Cemma-3-4B/122
A.X-3.1-7B/35A (Lab 2025), Kanana- 1.5-8B, Llama-3. 1-8 (Grattafiori et al. 2024), KONI-Ilama-
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3.1-8B (ISTI] 2024). and Qwen2.5-7B/14B [Qwnn et al. 2025). All experiments conducted with the instruction tunee versions of the models. See Appendix B. for further details on each model.
Training Settings All training runs minimum of 50,000 data points unless otherwise specified. Each experiment (including ablations) is trained for five epochs, except for A.X-3.1-35B, which we train for three epochs due to computational constraints. For further details the hyperparameters used throughout training, see Appendix B
3.2 EVALUATION DETAILS
Benchmarks In this work, we divide our evaluation suite into two parts: held-in set, used for routine monitoring during training and ablation studies, and a held-out set, evaluated once after all ablations and final training are complete. This is to support rapid iteration and prevent inadvertent overfitting to benchmarks during iterative training ablations.
Held-in consists of four benchmarks. MCLM (Son et al. 2025) is a translated collection of math problems from MATH500 and AIME2024, originally drawn from Olympiads, designed test deep chain-of -othhgthht reasoning rather than surface recall. IMMLU-Redux (Hong et al. 2025 is quality-controlled down-sampled version of KMMLU (Son al. 2024) that maintains correlations with the full suite while reducing evaluation cost; importantly, it spans both factual knowledge (c.g.. history, law, medicine) and maasoning-intenste science). HAE-RA RAE Bench (Son al. 2023) assesses Korean linguistic and cultural competence, covering vocabulary, reading comprehension, and historical content. For medical ablations, we also include ClinicalQA, Korean clinical QA benchmark derived from medical licensing examinations, consisting of problems based on chief complaints and medical specialties.
domains (c.g.. mathematics, engineering,
Held-out covers broader set of benchmarks used only after all training ablation is done. KMMLUU Hard (Son al. 2024) is adversarially filtered version of KMMLU for highest difficulty. KMMLU- Pro (Hong et al. 2025) contains expert-level professional licensure questions across 14 different categories, including Medicine, Finance, and Law. KSM (Ko et al., 2025) is a set of competition- style mathematics problems from Korean contests. CLIcK (Kim et al. (2024) aggregates factual questions from Korean exams and textbooks across 11 categories, providing measure of Ko- rean general world knowledge. Finally, KoBALT-700 (Shin et al. (205) is a finguistics-ocuused benchmark of 700 expert-written items that span syntax, semantics, morphology, phonology, and pragmatics, used to test fine-grand finengrained Korean linguistic competence.
Evaluation Setup All evaluations are run with vLLM (won et al. 2023) under the following configuration: t temperature=0.7, top_p=0.9, and mwx.t okens=32,nn3322... Models are instructed to present the final answer wrapped in \boxed boxed value; outputs without a valid answer are marked incorrect. All ablations use a single evaluation; for the main experiments, run three independent trials and report mean + standard error
}, and we use math-verify to validate the
4 LANGUAGE-MIXED CHAIN-OF-THOUGHT
When constructing multilingual reasoning data in a target language (other than English), a central question is how to represent the reasoning process: should it be written in the target language or left in English? Prior work has typically chosen one of two monolingual setups, either entirely in En- glish (Pipatanakul et al., 2025 Ha 2025 Son et al.) 2025) entirely in the target language (lightblue) 2025 Lee al. 2025a). Our initial exploration reveals critical shortcomings in both. Reasoning in English on Korean prompts introduces translation noise: prompts are often mistranslated, especially in culture-specific contexts, and over time, errors accumulate, leading the model to drift off topic once it "forgets" the original Korean wording. Conversely, reasoning in Korean produces notable drops in reasoning capability (Ko et al. (025), and extended training in Korean induces distributional drift in English-pretrained bases (Hong et al. 2024). degrading their original strengths.
wwwpiithu...ooo/hheeeeoete comm/uggiinn saagffaaae/aat-eerrree
See Scction C2 for more details.
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Table Language-Mixed CoT (ours) outperforms monolingual Co'Ts across models and sizes. Compared with English- or Korcan-only CoT, Language- Mixed CoT yields higher scores for both Gemma (4B) and Kanana (8B). Highest scores per column are highlighted in green. Abbreviations: HRBB HAE-RAE Bench; KMMLU-R -KMMLUU-Rdux.
Gemma-3-4B
Kanana-I.5-8B
CoT Lang.
HRB
MCLM
KMMLU-R
HRB
MCLM KMMLU-R
English
50.3
48.1
52.2
66.2
60.5
64.0
Korean
40.6
25.6
42.5
67.2
31.8
53.4
Language-Mixed.uuk
46.7
22.5
44.1
67.6
28.7
50.4
Language-Mixed puke
48.2
26.3
45.3
68.8
25.6
51.1
Language-Mixed, mnko
54.9
55.8
53.0
74.6
57.4
64.4
To address both issues, we propose Language-Mixed CoT (See Figure 1 for example). During the Think phase, the model code-switches, performing most logical scaffolding in English while preserving key Korean terms and quotations. This keeps faithfulness to the prompt without sacrificing reasoning power. To generate Language-Mixed COT, prompt the teacher preserve named entities, quoted spans, and key terms in Korean while generating the rest of the reasoning in English. After generation, we apply a regex-based filter to discard samples whose forean-characte ratio lies outside 5% and 20%. In Table 2 we train five variants: an English and Korean-only model, and three language-mixet models that combine Korean with English, Chinese, or Russian. The choice of Chinese and Russian follows Qi et al. (2025): Chinese is culturally and historically closer to Korean, whereas Russian relatively distant. Notably, language-mixed CoT with English anchoring outperforms other settings in most cases. Interestingly, Gemma3-4B shows gains on HRB and KMMLU-R even with Russian- or Chinese-anchored CoT, whereas Kanana- does not. We suspect this difference is driven by the pretraining mixtures: Gemma3-4B is multilingual model that includes substantial Russian and Chinese data, while Kanana- 1.5-8B is pretrained only on English and Korean. Most importantly, however, improvements MCLM (math) emerge only when using English-anchored CoT.
5 YI-SANG INSTRUCT
Despite many efforts to distill frontier models into smaller open models, only few manage to collect their own training corpus; most reuse or repackage existing datasets (Ye et al.) 2025 Guan et al.) 2025 Hugging Face 2025 Hu et al. 2025). This pattern also common in multilingual settings and materially affects outcomes: models trained through such pipelines lack robustness to everyday colloquial expressions that rarely appear in translated text. To pursue more robust multilingual reasoning, we decided to construct our own dataset. This section describes our instruction collection (Section 5.1). response generation process (Section 5.2) for building YI-SANG, and presents ablations used to derive the high-yield subset YI-SANG-HC (Section 5.3)
5.1 INSTRUCTION COLLECTION
Seed Instruction Collection. We curate native Korean prompts from public Q&A and community websites via two-step pipeline. (1) Source discovery. Using domain knowledge and targeted search, the authors compiled 54 candidate sites with user-posted questions and peer answers. Each site was assigned license category: (crawling and redistribution permitted), (crawling allowed but commercial use and redistribution prohibited), and C. (crawling prohibited). (2) Legal triage and crawling. We implement site-specific crawlers (one script per site) and exclude C sites, low-volume sources, heavily obfuscated structures, and near-duplicates. In this stage, remove 26 websites from the list. Data from B sites is used for training and analysis but not redistributed.
common practice refine web-collected seed instructions either with templates or LLM rewriting (Mishra et al.) 2021 Xu et al.) 2023) prior to training. However, we observe that such normalization removes user artifacts (typos, abbreviations, mixed script, and
Refinement and Filtering.
WWe use the term language-mixing in the same sense code-switching. that is, alternating between two languages within single context.
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Collected
Collected Questions
Collected Responses
7%
Vc 150
(24.2%)
3,0N
(32.75)
16
14
950
(25. 0%0
(14.8%) 8%
(54
1.7%
12 (18.2%)
2.0%
4.0%
esc Sac
(15.9%)
Code
Exams
Legal
Korean
Science
Daily
Fiaance
Medi cal
OpenThought
Figure 3: Category distribution across different stages of the dataset collection. (a) Sources (N=54); counts of the public Q&A and community websites we compiled; categories were manually assigned by the authors based on contextual review. (b) Questions:
crawling, items inherit the category from their source. (c) Responses: after response generation, we added Open' Thought (Guha et al. 2025) as an additional source. Colors are shared across panels; centers show total counts.
internet style) that harm robustness at deployment, so we keep prompts verbatim. We apply only light ratio below 30%, and drop prompts that
automatic filters: discard prompts with a are too short or too long (length- < 50 or > 8,192 characters). The Korean threshold was empirically chosen exclude fully non-Korean items while retaining mixed-language coding prompts.
Instruction Statistics. Figure BirreBBjinn iillustrates details on the collected sources and prompts. Initially, roughly 25. 25.9% of our compiled sources were legal websites. However, they tend to be small in scale legally restricted, they contribute only minor share to the total number of crawled questions. contrast, exam and daily communities host extensive, easily crawlable archives and overrepresented. Given that long chain-of-thought training primarily improves reasoning capabilities rather than those tasks involving knowledge retrieval (Yeo et al. 2025) Sprague et al. 2024), we prioritize STEM/Code/Exam categories in subsequent collection and curation.
Adding the OpenThought dataset. Finally, our web-sourced training mix lacks competition-leve problems that are known to cultivate reasoning ability (Guan al. 2025). We therefore add prompts
from Open (Guha et al. 2025) by translations through Gemini-2.5-Flaan (Comanici et al.
2025
Earlier attempts with GPI-4o-mini (Hurst et al.) 2024), Qwen2.5-72B Instructt (Qwen et al.
2025
and Seemini-2.0-Flan (Deepmind) 2024) produced training instabilities.
5.2
RESPONSE GENERATION
SFT over Reinforvement Learning. To build a strong Korean reasoning model, we focus on SFT in this work. Although recent studies report sizable gains from RL-based preference optimization (e.g., GRPO (Shao et al. 2024)), particularly for sub-32B models (Rastogi et al. 2025 Guo et al. 2025), these methods presume access strong base models (Wang et al. 2025 For Korean, such strong seeds are scarce, making RL vulnerable to the cold-start problem with unstable reward lcarning and poor exploration (Shao et al. 2025), Consequently, we prioritize SFT with curated data to build strong base model for subsequent RL efforts. Importantly, SFT has also been proven to be effective in training reasoning models (Hochlehnert et al. 2025 [i al. 2025). making it reliable first step.
Response Generation Methodology. To build the SFT dataset, we initially consider two strategies: (a) agreement-sampling where we sample multiple times from a teacher model and accept the first that an LLM judge (Zheng et al.] 2023) deems consistent with the web-crawled answer, and (b) hint-based refinement, where we prepend the crawled answer and ask the model to refine However, we find both concerning: (a) is prohibitively compute-intensive: (b) risks leakage, artifacts, and distribution shift that can hurt generalization. Moreover, web-scraped answers are unreliable. and the recent strong LLMs have chance surpass crowd responses. It should also be noted that several works (Toshniwal et al. 2024), including OpenThough (Guha et al. (025), and S1 (Muennighoti al. 2025), have empirically shown that response filtering is not necessary, hardly correlated with

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Augmentation
Teacher (Long CoT)
Teacher (Short CoT)
16,3% drop on performance without long reasoning
63.8
56.6
58.8
56.3
Avg. soore
49.5 49.3
48.6
45.9
48.5
39.7
40
40
20
20
Gemma-3-4B Kanana-1.5--8
Gemma-3-4B Kanana-1.5-88
Gemma-3-4B Kanana-1.5-88
Style
Oplion
Qwen3-32B
Qwen3-4B
Gemini-2.5-Pro
Qwen3-32B
Figure 4: Average scores across HAE-RAE Bench, MCLM, and KMMLU-Redux for Gemma-3-4B and Kanama-1.5 1-5-8B under three settings. (a) Augmentation. Option and Style are comparable on Gemma-3-4B (49.3 vs 49.5), while Option has a modest edge on Kanana-I 1aauma-1.5. 1,5-8B (58.8 vs 56.6); neither augmentation is uniformly superior. (b) Teacher (Long CoT)). Qwen3-32B yields higher averages than Qwen3-4B (Gemma: 51.8 48.6; Kanana: 63.8 56.3). Teacher (Short CoT). With shot CoT. Qwen3-32B tops 2.5 (Gemma: 41.8 39.7; Kanana: 48.5 > 45.9). Overall, Language-Mixed CoT and using Qwen3-32B teacher provide the strongest gains; both augmentation choices offer benefits.
the performance of the downstream model. We, therefore, choose regenerate all targets from the prompt alone with strong teacher, without any web-collected oracle.
Selecting Response Format and Teacher Model. To select the teacher model, we evaluate two candidates, Qwen3-328 and Qwen3-4B. We also test short-CoT setting, where the model is trained on plain instructional responses without explicit reasoning traces, similar to conventional instruction-tuning outputs. This variant implemented with Qwen3-32B (reasoning disabled) and 2.5-n Figure repoots the downstream results across teachers. As expected, Qwen3-32B with language-mixer CoT delivers the strongest performance. Notably, Qwen3-4B with reasoning surpasses both Gemini-2.5-PPr and Qwen3-328 without reasoning, highlighting the importance of explicit reasoning in unlocking L.LM capabilities. See Ablation A.4 for more details on training with different teachers.
Format Augmentation. The Exams category is highly standardized, typically a question with four options. To improve robustness beyond the fixed template, apply augmentations: (a) Style augmentation. We keep the question unchanged and prepend or append short stylistic directives ( Option augmentation. We use a BM25 retriever (Robertson et al. 1995) over the exam pool find similar questions and merge their distractor options with the original item. We drop items containing negation cues to avoid semantic flips, remove near-duplicate items, cap the merged list 10 options, and preserve the original correct option as the gold label. As shown in Figure training with either augmentation alone yields comparable performance, so we adopt both.
5.3 DATASET COMPOSITION
Building these lessons, prompts and augmentations. After filtering degenerations and enforcing Korean-ratio bounds, we obtain YI-SANG with 3.7M long-reasoning trajectories. However, while scaling data generally improves performance, multi-epoch training our full 3.7M instances is impractical due to limited compute budget. We therefore run targeted ablations to identify smaller, high-yield mixture.
Qwen3-32B to generate language-mixed CoTs for the 5.79M
What benefit does each category bring? We begin by training on each category a time. Each ablation run additionally includes 3,000 items from the Exams category to teach formatting. In Table we observe that OpenThought delivers the largest gains on MCLM. followed by Science and Code. Exams are the most effective source for HAE-RAE Bench and KMMLU- Redux. Notably, the Medical category appears highly specialized: it boosts ClinicalQA but significantly hinders performance on all other benchmarks. These trends hold across both models, suggesting that the most effective
Examples include: "return the final answer in \boxed() format", "output format: answer: <N>".
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Table 3: Contribution individual training categories. OpenThoughht and Exams provide the largest gains, followed by Code and Science. Medical boosts ClinicalQA but consistently harms performance on other benchmarks. Each run uses 50K examples from the target category, plus 3k EXAMS items for formatting. Since SCIENCE has only 37k examples, use 37k+3k without up-sampling. The highest-scoring model is highlighted green and the lowest-scoring model red, Abbreviations: Clin. ClinicalQA.
Gemma-3-4B
Kanana-1.5
Category
HRB
MCLM
KMMLU-R
Clin.
HRB
MCLM
KMMIJ-R
Clin.
OpenThoouht
54.9
55.8
53.0
62.1
74.6
57.4
64.4
73.97
Daily
54.2
34.9
51.9
62.4
69.1
36.4
58.7
70.0
Medical
50.5
20.9
49.4
65.6
64.5
28.7
57.0
70.3
Code
53.5
38.8
51.5
59.0
69.4
38.0
59.1
64.4
Exams
56.4
27.9
64.2
60.0
69.5
33.3
67.0
69.9
Science
52.2
37.2
52.1
61.9
41.1
58.8
67.5
mixture uses Though and Exams as the foundation and adds Science/Code for additional math robustness. Additionally, we find that scaling the Daily and Medical subsets has adverse effects on overall performance, leading us to exclude them from the final training composition.
Finalizing the dataset. Finally, we decide to train only with: OpenThought, Code, Exams, and Science. This approximates about 1.8M instances. To surface data issues, we conduct a one-epoch shakedown run with a proxy model (Kanana- 1.5mama-1.5-2.1B) 1.5-2.1B). We define "loss spike" as an abrupt rise in loss that does not recover immediately in the subsequent step. When such spikes occur, we locate the batch, manually inspect the items, implement a rule-based filter to remove the failure pattern from the entire dataset, and restart the run. This process is repeated until the loss curve stabilizes. During this process, we identify three recurring triggers: degeneration cases where responses endlessly repeat identical phrases; samples that contain multiple <think> ... </think> blocks; and instances in which the final solution after the </think> tag is written in a non non- Korean language. We also find that small number of extremely long reasoning traces disproportionnatly slow training, leading us to discard any instance exceeding 16k tokens.
Decontamination We decontaminate the training corpus against both held-in and held-out bench- marks using 13-gram overlap filter applied prompts and reasoning traces. Before constructing n-grams, we perform morphological segmentation and normalization with MeCab-Ko KO (MeCab-KO Contributors). We then run two passes: (i) build 13-grams over the normalized strings and (ii) build 13-grams over the raw text; any training instance that shares at least one 13-gram with any benchmark item in either pass removed, effectively eliminating exact and near duplicates. We intentionally avoid embedding based decontamination. as exhaustive semantic matching over 3.7M trajectories and nine benchmarks would computationally costly and risks discarding legitimate background knowledge rather than true leakage. Overall, the 13-gram decontamination removes about 0.7% of trajectories (-)5.9k). After all filtering steps. the finalized YI-SANG-HE corpus contains 260k instances, composed of 62k from OpenThought, 86k from Code, 37k from Science, and 66k from Exams.
6 RESULTS
YI-SANG-HQ Achieves State-Of-The-AA Performance. KO-REAson-35B, based on A.X-3.1 and trained on SI-SANG-HO. outperforms state-of tht reasoning models of comparable scale, including GPT-OSS-200 (Agarwal et al.) 2025b), 1eepSeek-R113228 (Guo al. 2025). EXAONE- Deep-328 (Research et al. 2025), and QwQ-32B (Team, (025). In Table 4 across nine benchmarks, KO-REAson-35B achieves the best performance on five tasks and ranks second on the remaining four, achieving the highest overall average. We note that performance on competition-level math datasets (AIME2024, KSM) trails GPT-OSS-200 which we attribute to the relatively small amount of competition-stee style reasoning data in our mixture: only ~ 60k translated OpenThought items after filtering, compared nearly 1M competition style problems in the original OpenThought project and ~ 0.5M in Liu et al. (2025b). Nevertheless, KO-REAson-351 ranks second place on both benchmarks while relying primarily on web-collected data that constitutes the majority of training.
"See Scction A.4 for details.
8

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Published as a conference paper at ICLR 2026
Table 4: Comparison of 20B+ reasoning models. KO-REAson-35B (ours) matches state-of-the-art peers similar scale while using only openly available data and code. Entries are reported as meansg over vt = independent runs, Bold marks the row-best; underline marks the second-best best. When standard-err -rror intervals overlap, ties so-highlighted. Exact prompts are provided in the supplementary materials, Math (Ko) and AIME24 (Ko) subsets of MCLM.
Category
Benchmark
GPT-OSS-20B
DS-RI-32B
EXAONE-Dep-32e
QwQ-32B
KO-REAson-35B
KMMLU-Redux
67.6p.1
70.01.6
68 08.22.2
74.71.0
76 16.4
KMML.UPPu
42.90.5
45.7p.a
43.51.8
51.01.1
47.40.G
KMMLU-Hard
39.0p.2
43.31.0
43.31.
49.0r.a
51.4p.s
Math
82.81
85.4g.1
84.82.0
82.3o.7
87.5o.a
Reasoning
AIME2024 (Ko)
71.16
51.77.1
58.311.8
53.39.1
66.711.3
KSM
72.14.7
62.8s.1
66.717.9
00.514.4
66.7s.8
HRB
65.1p.7
70.8o.4
76.10.3
75.51
78. 78.9p.7
Ko-Specific
CLIcK
57.2p.7
66. 6p.6
67.60.a
70.90.e
70. 70.90.9
KoBALT
31.01
33.3p.1
32.6p.6
37.72.0
34.91.0
Average
58.81.2
56.44.5
57.4s.
59.6g
64.02.6
Table 5: Performance of nine models (4B-35B) trained YI-SANG-HQ. The benefits of YI-SANG-HQ are consistent across model families and parameter scales. Results are meanse over independent runs. Cases where performance drops after training (without overlap standard errors) are highlighted. Ahbreviations:
K.M.-R KMMLU-Redux; K.M.-P KMMLU-Pro; K.M.-H KMMLUU-ard.
Model
K.M.-R K.M.PP
K.M.H
MATH
AIME24
CLIcK KoBALT
5B Models
Gemma-3-44
40.71.7
26.71.a
19.4,.2
41.925. 69.71.
1.72.
12.8
49. 15.2
45.94.
12 12.02.9
YI-SANG-HQ
65.5g.5
35.3g.6
41.65.2
15.02.4
38.813.
61.0g.9
55.2,,
20.0..1
Models
Qwen-2.5-7B
52.6p.s
34.0g.1
20.7 7..2
G8.1
6.7g.0
15.8p.3
00.41.1
50.9p.s
19.3p.s
YI-SANG-HQ
72.0p.
44.6g.5
46.7o.1
77.3p.7
41.711.
49.71.s
65.0gs
61.0p.4
24.11.4
A.X-3.1-7B
62.4g.5
38.8u.3
36.32.0
48.218.9
34.6.9.6
17.31.s
71.3u.7
64.8u.4
25.0g.3
YI-SANG-HQ
70.00.9
39.0p.7
45.7o.s
82.82.
33.314.1
53.41.3
72.5p.9
62.0n.9
23.9u.7
XONI-Llama-3.1-81
20.7o.4
16.0g.6
9.70.4
18.72.1
3.3g.0
4.80.2
21.71.
21.90.4
0.50.2
YI-SANG-HQ
69.6g.1
39.6a.a
44.7a.
71.71.4
31.77.1
38.3n.4
58.3n.s
56.51.0
21.40.4
Llama-3.1-8B
40.01.2
23.81.3
19.5g.2
29.37.1
1.72.4
5.10.2
43.7p.1
41.5p.
8.1o..
YI-SANG-HQ
68.9p.2
38. 38.6g.4
45.3g.0
72.2
26.714.1
38.7 70.7
G8.3
54.90.4
18.9p.
kan-na-1.5-8B
53.74.
37.7a.2
27.2
54.5p.
10.0g.0
15.0g.1
70.3
63.5p.s
20.6u.3
YI-SANG-HQ
70.7p.5
39.90.7
44.80.5
67.714
30.0o.0
39.82.1
72.9u.9
64.0o.4
28.60.5
< 20B Models
Gemma-3-128
19.1p.s
39.90.3
29.8g.0
73.2
15.07.1
28.1
69.8p.2
62.2g.5
26.0g.1
YI-SANG-HQ
72.T0.9
43.21.2
47.10.1
75.30.7
35.07.1
46.1
68. 68.8p.2
64.6p.2
29.6o.1
Qwen-2.5-14B
24.41.
22.7a.s
14.0..o
64.624
8.3
20.7a.
20.7a.
31.5p.7
19.8p.a
YI-SANG-HQ
77.1p.7
50.0gz
51.5u.
81.81.
38.32.4
55.6s.4
74.5p.2
67.5g.3
34.51.3
<30 Models
A.X-3.1-35B
72.2p.2
47.30.7
44.2p.2
73.12.1
16.7s.a
26.8g.a
84.0u.s
76.6g.c
35.5g.2
YI-SANG-HQ
76.0p.4
47 47.4p.6
51.4p.s
84.5p.5
66.711.5
65.7s.6
78.9,.7
70.9p.a
34.91o
This underscores the quality of the newly collected user prompts. We leave to future work on incorporating larger volumes of translated competition-style data to push performance further.
YI-SANG-HQ Demonstrates Persistent Gains Across Model Size and Family. To further validate the efficacy of YI-SANG-HO across diverse settings, we train nine models spanning 4B to 35B parameters from six different model families. Improvements are consistent across both scale and architecture, with especially pronounced gains on math-intensive benchmarks such as Math (Ko). AIME2024, and KSM, where models of all sizes benefit substantially. Korean-specific tasks (HRB1.0 CLIcK, and KoBALT-700) also show steady improvements, underscoring the value of YI-SANG-HQ's curated multilingual and culturally grounded data. General knowledge evaluations (KMMLU- Reddux KMMLU-Pro, KMMLU- --ard)) likewise improve, further demonstrating the broad coverage of the dataset. Performance degradation is observed only two cases, both with marginal drops less than two points. Overall, YI-SANG-HO proves to be versatile and widely applicable training resource,

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Published as a conference paper at ICLR 2026
capable of boosting models across families and scales, and offering substantial value for future research in multilingual and reasoning-foeuue focused LLMs.
Gemma3-128
KO-REAson-128
Cross-Lingual and Multi-Modal Free Lunch. To in- vestigate whether post-training on YI-SANG-HQ yields broader generalization, we evaluate Gemma3-12B and its post-trained variant (KO- 200-RRAAson-1200 12B) on two English reasoning benchmarks (AIME-2025, short-form math; GPQA, STEM MCQA (Rein et al.] (0244) and two Korean vision-language benchmarks AKA--- short-form STEM reasoning (Lee et al. 2025b); HAERAE-Visitee commonsense reasoning). KO-REAson- 12B outperforms the base model all four, indicating both cross-lingual and multimodal gains. We attribute the English improve- ments to two factors: (i) the benchmarks emphasize largely universal math and science knowledge, which facilitates transfer across languages, and (ii) Language-Miix CoT includes English reasoning steps that push general reasoning capabilities. English performance gains are con- sistent over all trained models, see Table 14 for more details. Additionally, we also observe gains on visual rea- soning despite no image data in post-training, consistent with prior reports of "multi-modal free lunch." (Choi al.) 2024 Rastogi et al. 2025) However, the transfer ap- pears selective, with strong gains on reasoning-hewe tasks and limited benefit shallow, factoid-style evaluations. See Appendix D.2 for complete results and experimental details.
45 1
Accuracy
20
15.6
long-form
AIME25
GPOA
Accuracy
15.47
10
0.73
2.06
KAIO-2
HAERAE Vision
Figure 5: Performance of Gemma3- 12B and its post-trained variant English reasoning benchmarks and Korean mul- timodal benchmarks. KO C-O-RASSON-IE trained only with text supervision, shows con- sistent gains across all tasks, indicating both cross-lingual and multimodal transfer.
CONCLUSION
In this work, present practical recipes for building reasoning models for mid-resource languages through a Korean case study. We introduce Language-Mixed CoT and curate 5.9M native-authored Korean prompts, underscoring the value of better supervision signals and high-quality local data. Using Qwen3-32B as the teacher, we construct and release YI-SANG, the largest publicly available Korean training resource. Its high-yield subset, YI-SANG-HQ, delivers consistent gains in general knowledge and reasoning across six model families spanning 4B-35B parameters, rivaling mod- els trained on proprietary data. We hope our work benefits Korean practitioners and the broader multilingual community, offering guidance for training their own reasoning LLMss
ACKNOWLEDGMENTS
This research was supported by the Korea Institute of Science and Technology Information (KISTI) 2026 (No. (2LSTI)),L,3MICI), aimed at developing KONI (KISTI Open Neural Intelligence), large language model specialized in science and technology. This work was partly supported by an Institute of Information & communications Technology Planning & Evaluation (ITPP) grant funded by the Korean Government (MSIT) (No. RS-2021-1121 1343, Artificial Intelligence Graduate School Program (Seoul National University)), the National Research Foundation of Korea(NRF) grant funded by the MSIT (RS-2024-00354218)
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A
ADDITIONAL DETAILS ON YI-SANG.
A.1 ORIGIN
Our dataset takes its name from Yi Sang (1910-1937; pen name of Kim Hae-gyeong), a Korean modernist and architect, known for his mathematically inflected literature. He employed geometric notation, numerical sequences, and experimental layouts into Korean literacy works. The name reflects our focus on formal Korean reasoning. Yi Sang also echoes Korean noun, meaning "the most complete state," consistent with our goal to create the strongest reasoning dataset.
A.2 PROMPTS
Figure 6 presents the system prompt used throughout the paper to generate Language-Mixed CoT from teacher models. We notice that longer and more detailed instructions are likely to constrain stylistic diversity of responses. Therefore, we keep the prompt simple possible.
Think carefully, do not translate the question while solving. Preserve the question in Korean so that you keep all details without adding noise. After you finish thinking, state your answer in fluent and coherent Korean.
Figure 6: System prompt used for dataset generation.
A.3 LICENSE
In Table d we detail the license of our trained models. The models will be made available on HuggingFace Both datasets YI-SANG and YI-SANG-HQ will be made available under the MIT License.
Table Summary Base models, upstream licenses, our trained model names, and release licenses. resort to the most open license possible.
Base Model
Upstream I. License
Trained Model (ours)
Release Llcense
Gemma3-4B
Gemma License
KO-REAson 13--RAAsn--G-44--888
Gemma License
Gemma3-122 12B
Gemma License
KO-REAson R---RRAo---------0000
G3-12B-1002
Gemma License
L-ama-3.1-BB
Llama3 Community License
KO-REAson -REAson-L3.1-SB-U L3.1-8B-0831
Llama3 Community License
KONI-LJama-3.I-80
Llama3 Community License
KO-REAson KI,3 1-8B-0831
Llama3 Community License
A.X-3.1-Light
Apache 2.0
KO-REAson-AAS BLEEAsoo-AX3.------UB-
Apache 2.0
1.X-3.1
Apache 2.0
KO- 1Al.ssoon-X31--588--
Apache 2.0
Qwen2.5-7B
Apache 2.0
KO-REAson-QQ Q2.5-7B-0831
Apache 2.0
Qwen2.5-141
Apache 2.0
R---REAAs-OOOO REAssn-Q2_5-1445 Q2_5-14B-1000
Apache 2.0
Kananal.5-ot
Apache 2.0
KO-REAson--A REAssR--2500--BB- K2505-8B-0831
Apache 2.0
A.4 ADDITIONAL ABLATIONS
Training with different Teachers. To investigate whether our LM-CoT distillation pipeline is agnostic to the choice of teacher, we apply the same procedure using both DEEPSEEK-R 1-32B and QWEN3-32B as teachers, and distill into two students: KANANA-L.. 1.NANA--1.5875 1.5-8B and GEMMA3-4B Table Summarizes the results. For KANANA-I 1.B.8B. supervision from DEEPSEEK- R1-3BB improves HAE-RAE Bench MCLM KMMLU-R from 60.8 / 45.7 / 48.1 to 71.0/ / 48.8 58.9, while QWEN3- 32B yields even larger gains (74.6/ / 57.4/ 64.4). For GEMMA3-4B, DEEPSEEK-RP 1-32B provides modest improvements, mainly on MCLM and KMMLU-R, and QWEN3-32B again delivers the strongest student, raising performance 54.9 55.8 53.0. These trends indicate that our pipeline consistently benefits different base models and teachers, while the absolute student performance remains bounded by the capability of the chosen teacher.
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Table Teacher and student performance on HAE-RAE Bench, MCLM, KMMLU-R. teachers
DeepSeek-RI R1-32B (DS) and Qwen3-322 (Q3) yield performance gains, proportionate to their original perfor- mance.
Models
HAE-RAE Bench
MCLM
KMMLU-R
Student Model Performance
(Base) Kamana-1 1..aaa
60.8
45.7
48.1
Supervised by DS-R1-32B
71.0
48.8
58.9
Supervised by Q3-32B
74.6
57.4
64.4
(Base) Gemma3-4B
53.5
43.4
38.7
Supervised by 11-R1
53.3
45.7
49.6
Supervised by Q3-32B
54.9
55.8
53.0
Teacher Model Performance
DeepSeck-R1-328 Qwen3-32B
71.8
75.2
70.2
75.7
83.7
81.0
Table 8: Size ablation on the Medical subset. Dou- bling the Medical subset from 50k to 100k leads to negative performance effects. Reported values show the change accuracy; Avg (non-Clin.) is the unweighted mean non-clinical benchmarks.
Scaling the Medical subset. To test for emer- gent gains, we double the Medical subset from 50k to 100k and retrain. Table 8 reports the per- formance gains relative to 50k. Gemma-3-4p de- creases all benchmarks, with the largest drop on ClinicalQA. Kanana- 1.5-8B exhibits near-zero changes. Therefore, exclude the Medical cat- egory from the final training mixture.
Model
A Avg (non-Clin.)
A ClinicalQA
Gemma-3-Bi
-0.76
-2.30
Kanana- Manana-1.5-88
+-0.09
+0010
Scaling the Daily subset. Table 3 shows that
Daily rarely leads any benchmark. We scale Daily by (20. 50, 100] and mix 15k instances each from from showing deflated scores on the academic benchmarks, since the Daily category is likely to lack academic value, As reported in Table performance consistently drops as we scale. Therefore, we also exclude the Daily category.
and Exams. The two datasets are added to prevent downstream models
Size ablation on the Daily subset. Overall performance declines as the subset increases in size. This may partly stem from limited benchmark coverage; nonetheless, evidence not enough tolcrate consistent drops across the remaining benchmarks. The tighest-scoring model is highlighted green.
Table 9:
Gemma-3-4B
Kanana-L.5-8h MCLM KMMLU-R
Data Mix
HRB
MCLM KMMLU-R
HRBB
2:1.5:1.5
56.2
48.8
54.0
73.1
48.8
60.8
5:1.5:1.5
55.5
48.1
68.9
45.7
59.7
10:1.5:1.5
55.8
40.3
51.6
69.9
43.4
58.8
Scaling to bigger dataset. We also investigate the effect of scaling larger training set. We conduct a controlled experiment with subset of 780k samples, consisting of YiSang-HQ combined with 500k English Open'Toupe instances (denoted YiSang-HQ+OTTe and fine tuned Gemma3-4B on this mixture.
Table 10: Effect of adding 500k English OpenThought samples on benchmark performance.
Benchmark
YiSang-HQ
HiSang-HQ+OT(cm)
KMMLU-Redus
65.3
63.5
HAF-RAE Bench
61.0
59.4
MCLM-K Ko
55.0
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As shown in Table 10 adding large amount of English Open data leads to substantial improvement on MCLM- (from 55.0 to 6), a math focused benchmark that particularly benefits from additional Olympiad style problems. However, this comes the cost of lower performance on KMMLUU-Rdux and HAF-RAF, both of which contain significant amount of Korean specific content. In other words, the larger and more math heavy mixture shifts the model toward stronger mathematical reasoning, while slightly degrading its overall Korean performance. Given that our primary objective build a balanced model for Korean usage, rather than optimizing a specific subset of reasoning benchmarks, we chose not to adopt this larger mixture in the final training runs. Nevertheless, for applications that prioritize reasoning performance math and related benchmarks, extending the training data with additional English Open Though style problems, as in YiSang- HQ+OT(en), appears to promising direction.
A.5
ABLATION DETAILS
Table 11 and provide detailed results behind Figure
Table 11: Comparison of augmentation strategies (style and option); no single method demonstrates a clear advantage. The highest-scoring model highlighted in green.
Gemma-3-4B
Kanana-L.5--8
Augmentation
IRBB
MCLM KMMLU-R
HRB
MCLM
KMMLU-R
Style
56.4
27.9
64.2
69.5
33.3
67.0
Option
55.8
30.2
61.9
72.8
37.2
66.5
Table 12: Comparison of different teacher models and response formats. Training long chain-of- thought reasoning generated by Qwen3-32B shows best performance. Performance caps are most pronouneed the MCLM benchmark, implying its effectiveness in boosting reasoning performance. The highest-scoring model is highlighted in green.
Gemma-3-4B
Kamana-1.5-8P
Teacher Model
HRB
MCLM KMMLU-R
HRB
MCLM KMMLU-R
Language- Mixed CoT
Qwen3-32B
54.4
48.1
53.0
73.1
57.4
60.8
Qwen3-4B
48.6
45.0
52.3
67.8
41.9
59.1
Solution Only (Short CoT)
Gemini-2.55 2.5-Pro
49.5
25.6
44.1
67.6
24.0
46.2
Qwen3-32B
51.3
28.7
45.3
68.5
23.3
53.7
B ADDITIONAL DETAILS ON MODEL TRAINING.
B. MODELS
Gemma-3 TTam et al. 2025) Google's third- soiddggeneration open model family. We use 4B and 12B instruction- -unied variants. Gemma-3 is multimodal model (text and vision), though in this work we use purely for text. The 4B version is pretrained on roughly 4T tokens, and the 12B on about 12T tokens. It is massively multilingual, covering more than 140 languages without a special focus on any single one.
2025) is built by Alibaba Cloud and trained on up to 18T tokens. is Qwen-2.5 (Qwn et al.
grounded primarily in Chinese and English, but demonstrates solid multilingual capabilities with decent coverage of Korean (Hong et al. 2025). In our experiments, we use both the 7B and 14B instruction-tuned variants.
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A.X-3.1 LLb 2025 is family of LLaMA-style models developed by SK Telecom with particular focus on Korean. It is trained on approximately 2.1 trillion tokens and achieves top-tier scores on Korean benchmarks, such as KMMLU, while still performing well in English. We employ both the 8B and 35B variants.
Kanana-1.5-8B (Bak et al. 2025) is a bilingual -nglish-Korean model, trained by Kakao, with an 8B parameter LLaMA- -tyle transformer. is trained on about 3T tokens, with more than 10% Korean content, while the rest is primarily English. The training recipe includes staged pretraining and efficiency optimizations.
Grrtttafiori al. 2024 is trained on approximately 15T tokens and designed as multilingual model but with emphasis eight major languages, inc including English, German, French, Italian, Portuguese, Hindi, Spanish, and Thai. Although is broadly multilingual, remains relatively English-centric.
1lama-3.1-8B-Insttee
KONI-Llama-3.1-88 KISTI 2024) is continual pretrained variant of Llama-3.1 3.1 developed by KISTI. It starts from the base Llama-3. 3.1BBBBBBB architecture and undergoes continued pretraining on 0.5 trillion tokens of additional Korean text and domain-specific corpora in science and technology.
B.2 HYPERPARAMETERS
Training hardware spans from eight NVIDIA H100 twenty-four NVIDIA H200 GPUs. Ablations use epochs, global batch size of 128, b precision, and AdamW (learning rate 2 10-3 with 10% warmup; weight decay x 10-5). Loss is computed only on reasoning traces and solutions. We employ PyTorch FSDP, Liger kernels (Hsu et 2004), and FlashAttention-2 (Dao et al. 2022).
For the final runs on YI-SANG-HE HQ we scale the global batch size to 512.
B.3 PACKING
We train Gemma-3-4B and Kanana- 1.5-8B on YI-SANG-HI HQ under two settings (with vs. without packing). Although packing provided substantial speedups, shown in Table 13 we observe measur- able drops trained without packing.
and reasoning benchmarks; accordingly, all reported models are
Gemma-3-4B
Kanana- 1.5-BB
Benchmarks
w packing
wo packing
packing
wo packing
KMMLU-Redux
62.87
64.19
70.06
71.30
HAE-RAE Bench
59.62
55.33
75.88
73.73
MCLM-Ko
55.04
58.91
62.02
65.12
Training Time
576
1728
1296
3360
Table 13: Comparison of model performance with and without packing.
C
ADDITIONAL DETAILS ON EVALUATION
C.I PROMPTS
Figure is the prompt used for evaluation.
ボ प.t F.
a| ghe t 8 Itgla 7AI_a: \boxed
Figure 7: System prompt used for evaluation on Korean henchmarks.
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C.2 PROCESSING DETAILS
We extract each model's final answer from the first \boxed ( .... that appears after the model's hidden "think" (reasoning) content. Any \boxedí... }. strings that occur inside the think section ignored. If multiple {boxed
}. entries appear in the visible answer, always take the first one and disregard the rest, even they contradict one another. An answer is credited only if this first post-think \boxed( ), is parsable. If the model fails to produce parsable \boxed the response is marked incorrect, even when the correct value appears elsewhere in plain text.
If generation runs the maximum token limit and parsable \boxed( (typically due to degeneration). the item is marked incorrect. By contrast, if generation is interrupted before reaching the token limit due hardware runtime failure, we the same prompt once with the same decoding settings; the score is based on the retry.
is produced
D ADDITIONAL RESULTS
D.1 CROSS-LINGUAL GAINS ON ENGLISH BENCHMARKS
Table 14: Performance of nine models (4B-35B) trained on YI-SANG-HO. Results are meanse over n=3 runs on AIME24, AIME25, and CPQA. The benefits of YI-SANG-HQQ are consistent across model families and scales.
Model
AIME24
AIME25
GPQA
5B Models
Gemma--344
6.7a.a
10.0g.8
19.52.9
YI-SANG-HQ
23.317.3
22.2g.9
32.27.7
10B Models
Qwen-2.5-7B
6.7u.e
7.81.9
27.13.5
YI-SANG-IIQ
41.11.9
34.41.8
43.11.3
A.X-3.1-7B
13.30.0
13.35.8
25.63.7
YI-SANG-HQ
46.75.8
31.11.9
37.72.9
KONI-Llama-311-88
0.0u.a
0.0p.a
14.11.5
+ YI-SANG-HQ
21.11.9
32.21.9
39.20.6
Llama-3.1-8B
0.0b.a
0.0p.a
19.20.6
+ YI-SANG--OO
28.94.8
21.11.9
40.20.8
Kanana- 1.5-BB
5.61.a
12.21.
31.12.5
YI-SANG-IIQ
25.67.7
27.81.9
38.9u.6
208 Models
Gemma-3-12B
13.3p.0
15.61.9
32.01.2
YI-SANG-HQ
42.27.7
30.05.8
45.16.7
Qwen-2.5-14B
7.86.1
13.33.3
26.31.3
YI-SANGI-HQ
41.115.0
42.210.2
51.76.6
308/ /5B Models
A.X-3.1-35B
15.63.a
15.61.9
37.00.8
YI-SANG-HQ
58.916.4
53.312.0
47.8s.3
Alongside the results in Table 5 we also observe consistent gains on English reasoning benchmarks such AIME2024/2025 and GPQA (Rein et al. 2024). While the improvements to rival state-of-the-art systems of similar scale, it is notable that every model improves across all English benchmarks despite never seeing English prompts during training. We attribute this two factors. First, the math and science benchmarks used here largely test universal knowledge, making them less dependent on the training language and enabling transfer from the Korean supervision. Second, the proposed Language-Mixed CoT likely helps models maintain alignment with their original English distribution, since they continue to practice reasoning partly in English. These findings highlight promising directions for further study on cross longull transfer in reasoning.
yet sufficient
D.2 CROSS-MODAL. GAINS ON VISUAL LANGUAGE BENCHMARKS
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Earlier works have discovered the "multi-modal free lunch", noting that Visual Language Models ( (VLMs) trained with text-only reasoning data often improve across a wide range of vision benchmarks (Choi et al. 2024 [i et al.. 2025b). We extend this line of inquiry by evaluating Gemma3-12B, a model with a visual encoder but trained solely on YI-SANG-HO, across three multimodal benchmarks: K- V-sicuui (knowledge- focused, MCQA) (Park al.) 2024), HAERAE-
Cemmes3120
Gemmet-377
76.7
72.1
-4%
+1.99%
22.5
Vision (reasoning. long-form) (Choi et al. 2026).
and KAIO-2 (STEM/reasoning, short-form) (Lee et al. 2025b). As shown in Table KO-REAson-T22 achieves notable gains on reasoning-oricnttee tasks
30
KAIO-2
HAERAE Vision
despite lacking vision training. Unlike prior reports of across-the-board improvements (Rastogi et al. 2025). however, find that shallow factoid-style bench- marks such as K- Kisccut see little to no benefit. This suggests that the free lunch of text-based reasoning transfers selectively: boosting reasoning-heas multi-
Figure 8: Accuracy on K-Viscuit, KAIO-2, and HAERAE- AAAAAE-Viiion for Gemma3-12B, Gemma3- 27B, and KO-REASON-12B, KO-REASON-12B post-trained variant of Gemma3 12B on YI- SANG-HQ.
modal tasks, but not those requiring surface-level factual recall.
D.3 IMPORTANCE OF HELD-IN BENCHMARKS AS PRACTICAL PROXIES.
While we apply n-gram filter for decontamination, our iterative process of retraining to refine subsets inevitably uses held-in benchmarks as proxy for progress. This raises the theoretical concern of gradu- ally overfitting held-in metries. However, we view this practice as a necessary and near-optimal com- promise: without reliable proxy, it would be possible to guide dataset construction effectively. Im- portantly, we do not advocate abandoning the distinc- tion between held-in and held-out splits; both remain essential for fair evaluation. In practice (Figure 9 we find that performance gains are indeed larger on held-in benchmarks. Still, should also noted that gains smaller higher baselines overall, and part of the difference reflects the greater difficulty of held-out benchmarks. Crucially, Table 4 and Ta- ble [5] show that models trained on YI-SANG-ER AAQ consistently improve across all benchmarks, includ- ing unseen held-out targets. This confirms that, de- spite mild contamination risk, our procedure achieves generalization while ensuring stable progress during training.
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Figure 9: Comparison of gains in Held-In/Out benchmark suites. Each point is (model, bench- mark) pair, x-axis shows the baseline score (%), y-axis shows the improvement after training on the YI-SANG dataset. Green circles are Held-In benchmarks; red squares are Held-Out benchmarks (others). Solid/dashed lines OL.S fits.
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Reviews:
[{"review_id": "uogC0zYjzU", "reviewer": "Reviewer_BzTR", "summary": "The paper presents Language-Mixed Chain-of-Thought (CoT) as a multilingual reasoning strategy and introduces Yi-Sang-HQ, a 260k-example Korean reasoning dataset generated under this schema. The work aims to enhance the robustness of reasoning for non-English languages and to demonstrate that open-source pipelines can rival those of closed-source systems. The authors conduct small-scale ablations on language-mixing (Table 1), category-wise analyses for dataset composition (Table 2), and large-scale fine-tuning across nine model families and parameter ranges (Table 4).", "questions": "1. According to Weakness 1: Could the authors clarify the scientific enquiry that this dataset enables? It would strengthen the paper if the authors could explicitly articulate how the dataset can be used to investigate knowledge-generating questions about multilingual reasoning—such as what reasoning patterns transfer across languages, or how mixed-language supervision affects reasoning generalization.\n2. According to Weakness 2: as of now, I think only the first and third contributions are valid. The presentation will be strengthened by focusing on the dataset contribution, while ablation studies serve as design support rather than a standalone contribution. Would the authors agree with this statement?\n3. On the usage of the term \"language-mixed\" vs \"code-switched.\" When I first read the title (and even the abstract), I thought the paper would be about LLM reasoning in one language while answering in another language (Fig.1 helps clarify this though.) I'm not sure if code-switching would be a better word choice for this paper?", "limitations": "", "rating": 4, "confidence": 3, "soundness": 2, "presentation": 2, "contribution": 2, "strengths": "1. Strong practical motivation and relevance. Addresses the real problem of reasoning degradation in translated multilingual data and proposes a scalable, open alternative.\n2. High-value resource contribution. The proposed Korean reasoning corpora, Yi-Sang-HQ, is well-engineered, with transparent filtering and selection procedures.\n3. Conceptually interesting reasoning schema. Language-Mixed CoT is novel and intuitive, supported by small-scale ablation results.", "weaknesses": "1. Unclear Statement of Enquiry. It’s difficult to judge the scientific merit of this work without a clearly defined statement of enquiry. The closest thing to it appears in the first sentence of Section 4: “When constructing multilingual reasoning data in a target language (other than English), a central question is how to represent the reasoning process: should it be written in the target language or left in English?” However, no question guiding the inquiry is stated in Section 5. In other words, the authors do not propose how this dataset can be used to perform any knowledge-generating investigations. Yi-Sang-HQ is presented as an engineering resource to improve model performance, not as a scientific instrument for discovering new insights about multilingual reasoning.\n2. Small-Scale Experiment to Verify Language-Mixed CoT. Given that Language-Mixed CoT is presented as one of the contributions, readers might expect an extensive empirical investigation of whether reasoning traces should be written in English, the native language, or a mix of both. In practice, Table 1 is intentionally limited in scope; it involves only two small models and a few held-in benchmarks, operating within the confines of an ablation study whose role is to guide dataset construction. This design choice is reasonable, but it also means the experiment serves as design verification rather than as a standalone empirical contribution.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "qxjwOucHrs", "reviewer": "Reviewer_oqNR", "summary": "This paper proposes a hybrid language thought chain (CoT) supervision scheme that alternates between English \"anchoring\" and the target language (Korean) for thought training to maintain reasoning ability while reducing translation noise. The paper also constructs a large Korean post-training corpus, YI-SANG (5.79 million cue words --> 3.7 million long CoTs), and refines it into a high-yield subset (YI-SANG-HQ) containing 260,000 words through targeted ablation and filtering (e.g., removing traces longer than 16,000 lemmas; n-gram decontamination). Using this data, the paper trains nine instruction-tuned models across six series (4B–35B), achieving state-of-the-art results on nine Korean benchmarks using KO-REAson-35B. Furthermore, despite being trained only on text, the model is transferable to English reasoning and Korean VLM tasks.", "questions": "Based on the Weaknesses, several questions and suggestions are listed below:\n\n**Comparison to existing work** Include and discuss the distinctions with the existing work [1].\n\n**Anchor choice** It would be better to write more about the pros and cons of selecting English as the anchor. In practice, sometimes we may only have access to sufficient corpora of non-English languages, like a Switzerland company may only have training data in German, French, and some Italian, with less English data. Have you tried other languages as the anchor, and how do they affect accuracy and readability?\n\n**Teacher sensitivity** It would be promising to also attempt different teacher models (e.g., Gemini-2.5-Pro reasoning enabled, or open R1 variants) and see if the results or trends remain similar.\n\n**Beyond n-gram check.** In addition to n-gram filtering (n=13), semantic matching or exact match checks on each benchmark dataset will make the results more robust.\n\n---\n\n***Reference***:\n\n*[1] When Models Reason in Your Language: Controlling Thinking Language Comes at the Cost of Accuracy. Qi et al., 2025*", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "The idea is simple but effective. Language-Mixed CoT consistently beats monolingual CoTs across models/sizes (Table 1). The description and motivation are clear. Also, it introduces a large and native prompt corpus of 5.79M Korean user-authored prompts, 3.7M CoTs, downselected to a 260k high-yield mix with concrete rules. Thorough ablations are conducted. \nThe results clearly demonstrate that KO-REAson-35B ranks 1st on 5/9 and 2nd on the rest, and improvements persist across 4B–35B and multiple families. In addition, the paper also reveals that text-only training still helps English reasoning (AIME-25/GPQA) and Korean VLM reasoning tasks.", "weaknesses": "**Missing citations to relevant works & Positioning vs closely related work.** Qi et al. (2025) [1] show that forcing models to think in the user's language improves readability but can hurt accuracy, revealing a trade-off. They also try to force the model to reason in the target language via prefix-hacking. The work is closely related to this paper. Although there are differences, it would still be important to include and clarify the distinctions with that work.\n\n**Limited Choice of Teacher model.** Most long-CoT supervision relies on Qwen3-32B. It is promising to explore other teacher choices or alternative anchors (e.g., non-English).\n\n**Contamination/Overfitting Risk.** Although n-gram decontamination (n=13) is applied, the paper acknowledges iterative use of held-in benchmarks as proxies and shows larger gains on held-in vs held-out. More contamination checks (exact-match, semantic) would increase the solidity of the findings.\n\n---\n\n***Reference***:\n\n*[1] When Models Reason in Your Language: Controlling Thinking Language Comes at the Cost of Accuracy. Qi et al., 2025*", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "lOjLT8yDfi", "reviewer": "Reviewer_Rtnv", "summary": "This paper presents a comprehensive case study on building strong reasoning models for Korean, a mid-resource language. Its primary contributions are: (1) the introduction of \"Language-Mixed Chain-of-Thought,\" a supervision method that allows models to reason primarily in English while preserving key Korean terms; (2) the curation and release of Yi-Sang, a large-scale (5.79M prompts, 3.7M reasoning traces) Korean instruction-tuning dataset sourced from native web content; and (3) the distillation of this into Yi-Sang-HQ, a high-yield 260k subset, used to train the KO-REAson model series (4B-35B), which demonstrates state-of-the-art or competitive performance on a suite of nine Korean benchmarks.", "questions": "(1) Could you provide more detail on the n-gram decontamination process? Specifically, what was the pass/fail criterion, and what fraction of the data was removed? \n\n(2) You show impressive gains from the 260k Yi-Sang-HQ subset. Did you observe any indications of performance saturation? Are there plans to scale training to the full 3.7M instance Yi-Sang dataset?", "limitations": "", "rating": 8, "confidence": 5, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "(1) The proposed \"Language-Mixed CoT\" is a thoughtful and empirically grounded solution to the known problems of translation artifacts (from English-only CoT) and degraded reasoning (from Korean-only CoT). The ablation studies in Table 2 robustly support its effectiveness.\n\n(2) The scale and nature of the Yi-Sang dataset fill a critical gap in the ecosystem. Moving beyond translated corpora to native, web-crawled prompts is a crucial step for building robust, real-world multilingual models. The detailed documentation of the data collection and refinement pipeline is highly valuable for the community.\n\n(3) The authors conduct over 100 ablations to justify key decisions (teacher model, data categories, augmentation strategies) and demonstrate the efficacy of their final dataset across nine different models from six families. This greatly strengthens the claims and provides a reproducible recipe.", "weaknesses": "(1) The comparison in Table 1 is excellent, but it would be beneficial to include a baseline that represents a more standard approach—for instance, a model trained on a directly translated version of a high-quality English reasoning dataset (like OpenThought)—to more directly isolate the benefit of native prompts and Language-Mixed CoT versus simply having more Korean data.\n\n(2) The process of down-selecting to the 260k Yi-Sang-HQ subset is well-documented, but the specific ratios chosen for the final mixture (62k OpenThought, 86k Code, etc.) feel somewhat arbitrary. A more principled approach (e.g., based on performance-density or optimal mixing ratios) would have strengthened this part of the methodology, though the strong end results justify the outcome.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "NePe3Jn6LN", "reviewer": "Reviewer_7kJM", "summary": "To improve mid-resource language reasoning, this paper introduces a new paradigm, language-mixed CoT, which switches between English and the target language. In this way, English acts as an anchor to excel in reasoning while minimising translation artefacts. To do so, the authors perform a study using Korean as an example, collecting a large number of questions from various scopes and the corresponding reasoning solutions generated from Qwen3-32B. Experimental results show that models fine-tuned on this dataset achieve substantial performance improvements.", "questions": "See above Weaknesses.", "limitations": "", "rating": 2, "confidence": 4, "soundness": 2, "presentation": 2, "contribution": 2, "strengths": "- The proposed paradigm, dataset collection recipe, and collected dataset may be of interest to sub-communities\n- The experimental setup is broad, covering a variety of model sizes (4B-35B) and different evaluation benchmarks", "weaknesses": "- In line 61, '...,leaving open how to achieve language-specific reasoning', which is a very vague statement. In fact, many works have proposed approaches to improve reasoning for low-resource languages, such as language alignment[1],  multilingual fine-tuning [2, 3], and cross-lingual fine-tuning [4]. Instead of focusing on a single language, these works study reasoning in a broader scenario, i.e., multiple low-resource languages. Also, the general pipeline of this work is to collect data and extract responses from a teacher model, followed by supervised fine-tuning. Overall, I think the novelty of this paper is very limited\n- In lines 222-223,  'To pursue truly multilingual reasoning,...', this is very vague. What is truly multilingual reasoning? This is not clear to me. If the authors consider language-mixed reasoning to be true multilingual reasoning, a rigorous definition or reference is needed.\n- The contribution is overclaimed. For example, the authors state that their models outperform closed systems, which are defined by training on closed data. If this definition is adopted, almost all current state-of-the-art models, including many labelled as open-source, would fall under the \"closed\" category.\n- Distilling data from larger models and using it to fine-tune smaller models makes sense, but I do not understand why data is used to fine-tune the comparable model (Qwen3-32B vs KO-REAson-35B) in this work. This shows that the teacher model (Qwen3-32B) is good enough.\n- Taking language-Mixed CoT as a more effective supervision signal is an interesting direction, while this paper lacks an in-depth related analysis and discussion. For instance, what specific aspects of language mixing contribute to better performance? Does it help bridge cross-lingual semantic gaps, improve cross-lingual reasoning consistency, or simply leverage the stronger reasoning ability in English?\n\n\n[1] Zhu et al. Question Translation Training for Better Multilingual Reasoning. ACL 2024 Findings  \n[2] Lai et al. mCoT: Multilingual Instruction Tuning for Reasoning Consistency in Language Models. ACL 2024  \n[3] Chen et al. Breaking Language Barriers in Multilingual Mathematical Reasoning: Insights and Observations. EMNLP 2024 Findings  \n[4] Chai et al. xCoT: Cross-lingual Instruction Tuning for Cross-lingual Chain-of-Thought Reasoning. AAAI 2025  \n\n\nTypos/suggestions:\n- Line 179: no space between text and footnote number\n- Line 194: table caption is too close to the above text\n- The caption of Figure 4 is unreadable, with too much text in bold; this figure is first referenced in lines 317-323, but some information (e.g. style and option) in the figure is introduced in the later part.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)