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Model: KOREAson/KO-REAson-AX3_1-7B-0831
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---
library_name: transformers
tags: []
---
# KO-REAson
**KO-REAson** is a series of Korean-centric reasoning language models developed in collaboration with [OneLineAI](https://onelineai.com/), [KISTI-KONI](https://huggingface.co/KISTI-KONI), [HAE-RAE](https://huggingface.co/HAERAE-HUB) and ORACLE.
We use the **Language-Mixed Chain-of-Thought (CoT)** approach, which allows the model to alternate between English and Korean during the “Think” stage of reasoning, preserving key Korean terms while leveraging English for logical scaffolding.
Top-performing models of our series [KO-REAson-AX3_1-7B-0831 (KONI-7B-R-20250831)](https://huggingface.co/KISTI-KONI/KONI-7B-R-20250831) and [KO-REAson-7B-Q2_5-0831](https://huggingface.co/KoReason/KO-REASon-7B-Q2_5-0831) show performance comparable to models trained on closed-source datasets such as Exaone-Deep-7.8B.
<p align="left">
<img src="https://cdn-uploads.huggingface.co/production/uploads/60d3e619b8448e1785bbda2a/uqrKdxbQEqAFknYBmuH7Y.png"
alt="Model Comparison" width="750"/>
<br>
<em style="display:inline-block; max-width:750px; text-align:cener; white-space:normal; word-wrap:break-word; line-height:1.5;">
<b>Left:</b> Average performance (Held-out-Ko) of open models trained on closed or open data;
our models are highlighted in green.
</em>
</p>
## Model Details
The **KO-REAson-0831** family comes in six variants based on the base model used.
| Model (link) | Base | Notes |
| -------------------------------------------------------------------------------------------- | -------------------- | --------------------------- |
| [KO-REAson-L3_1-8B-0831](https://huggingface.co/KoReason/KO-REASon-L3_1-8B-0831) | [Llama-3.1-8B](https://huggingface.co/meta-llama/Llama-3.1-8B-Instruct) | `L3_1` → Llama-3.1-8B |
| [KO-REAson-KL3_1-8B-0831](https://huggingface.co/KOREAson/KO-REAson-KL3_1-8B-0831) | [Koni-Llama-3.1-8B](https://huggingface.co/KISTI-KONI/KONI-Llama3.1-8B-Instruct-20241024) | `KL3_1` → Koni-Llama-3.1-8B; also called [KONI-Llama3.1-8B-R-20250831](https://huggingface.co/KISTI-KONI/KONI-Llama3.1-8B-R-20250831) |
| [KO-REAson-G3-4B-0831](https://huggingface.co/KoReason/KO-REASon-G3-4B-0831) | [Gemma-3 4B](https://huggingface.co/google/gemma-3-4b-it) | `G3` → Gemma-3-4B |
| [KO-REAson-AX3_1-7B-0831](https://huggingface.co/KOREAson/KO-REAson-7B-AX3_1-0831) | [A.X.-3.1-Light (≈7B)](https://huggingface.co/skt/A.X-3.1-Light) | `AX3_1` → A.X.-3.1-Light; also called [KONI-7B-R-20250831](https://huggingface.co/KISTI-KONI/KONI-7B-R-20250831) |
| [KO-REAson-K2505_8B-0831](https://huggingface.co/KoReason/KO-REASon-K2505_8B-0831) | [Kanana-2505 (8B)](https://huggingface.co/kakaocorp/kanana-1.5-8b-instruct-2505) | `K2505` → Kanana-2505 |
| [KO-REAson-7B-Q2_5-0831](https://huggingface.co/KoReason/KO-REASon-7B-Q2_5-0831) | [Qwen-2.5 (7B)](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct) | `Q2_5` → Qwen-2.5 |
# Performance
**Evaluation Datasets**
The model's performance was evaluated across a total of 11 benchmarks, and the evaluation suite is divided into two parts: (You can check these benchmarks in [HAERAE-HUB/KoSimpleEval](https://huggingface.co/datasets/HAERAE-HUB/KoSimpleEval))
- **Held-in**: This set of benchmarks is used for routine monitoring of the model's performance during the training and ablation study phases.
- **Held-out**: This set is used only once to evaluate the final model after all training and ablations are complete.
This separation is designed to prevent inadvertent overfitting to the benchmarks during the iterative training process and to provide a more accurate measure of the model's generalization capabilities.
|**Category**|**Held-in**|**Held-out**|
|---|---|---|
|**General Knowledge**|KMMLU-Redux|KMMLU-HARD, KMMLU-Pro|
|**Reasoning**|MCLM|KSM, GPQA, AIME2024, AIME2025|
|**Korean-specific**|HAE-RAE Bench|CLIcK, KoBALT-700|
**Comparison with models trained on public datasets**
<table>
<thead>
<tr>
<th>Models</th>
<th># Instances</th>
<th>Methodology</th>
<th>Held-Out (Ko)</th>
<th>Held-Out (En)</th>
<th>Total</th>
</tr>
</thead>
<tbody>
<tr>
<th>KO-REASon-AX3_1-7B-0831(KONI-7B-R-20250831; Ours)</th>
<td>260k</td>
<td>SFT</td>
<td><b>44.6</b></td>
<td>41.2</td>
<td><u>43.3</u></td>
</tr>
<tr>
<th>KO-REASon-7B-Q2_5-0831(Ours)</th>
<td>260k</td>
<td>SFT</td>
<td><b>45.10</b></td>
<td>38.75</td>
<td><u>49.95</u></td>
</tr>
<tr>
<th>KO-REAson-KL3_1-8B-0831(KONI-Llama3.1-8B-R-20250831)</th>
<td>260k</td>
<td>SFT</td>
<td>40.13</td>
<td>30.57</td>
<td>43.66</td>
</tr>
<tr>
<td colspan="6" style="text-align:center; font-weight:bold;">Open Recipe (En)</td>
</tr>
<tr>
<th>OpenThinker3-7B</th>
<td>1.2M</td>
<td>SFT</td>
<td>33.6</td>
<td><b>55.5</b></td>
<td>41.8</td>
</tr>
<tr>
<th>s1.1-7B</th>
<td>1k</td>
<td>SFT</td>
<td>35.6</td>
<td>23.4</td>
<td>31.1</td>
</tr>
<tr>
<th>Llama-3.1-Nemotron-Nano-8B-v1</th>
<td>&gt;3M</td>
<td>SFT &amp; RL</td>
<td>27.0</td>
<td>44.1</td>
<td>33.4</td>
</tr>
<tr>
<td colspan="6" style="text-align:center; font-weight:bold;">Open Recipe (Ko)</td>
</tr>
<tr>
<th>Ko-R1-14B</th>
<td>45k</td>
<td>SFT</td>
<td><u>43.7</u></td>
<td><u>46.3</u></td>
<td><b>44.7</b></td>
</tr>
<tr>
<th>Ko-R1-7B</th>
<td>45k</td>
<td>SFT</td>
<td>27.3</td>
<td>36.1</td>
<td>30.6</td>
</tr>
<tr>
<th>LLaMa-3.1-Ko-Reasoning-8B</th>
<td>63k</td>
<td>SFT</td>
<td>17.7</td>
<td>7.7</td>
<td>14.0</td>
</tr>
</tbody>
</table>
**Held-out benchmark performance**
<table border="1" cellspacing="0" cellpadding="6">
<thead>
<tr>
<th rowspan="2">Model</th>
<th rowspan="2">Model Size</th>
<th colspan="2">General</th>
<th colspan="4">Reasoning</th>
<th colspan="2">Korean-Specific</th>
<th rowspan="2">Average<br>(Held-out)</th>
<th rowspan="2">Average<br>(Held-out-Ko)</th>
</tr>
<tr>
<th>KMMLU-HARD</th>
<th>KMMLU-Pro</th>
<th>KSM</th>
<th>AIME 2024</th>
<th>AIME 2025</th>
<th>GPQA</th>
<th>CLIcK</th>
<th>KoBALT-700</th>
</tr>
</thead>
<tbody>
<tr>
<td><b>Llama-3.1-Nemotron-Nano-8B</b></td>
<td>8.03</td><td>21.47</td><td>22.89</td><td>47.06</td><td>56.67</td><td>43.33</td><td>32.32</td><td>34.54</td><td>9.29</td><td>33.45</td><td>27.05</td>
</tr>
<tr>
<td><b>Llama-3.1-Korean-Reasoning-8B-Instruct</b></td>
<td>8.03</td><td>14.91</td><td>21.72</td><td>6.09</td><td>0.00</td><td>0.00</td><td>23.23</td><td>39.65</td><td>6.14</td><td>13.97</td><td>17.70</td>
</tr>
<tr>
<td><b>EXAONE-Deep-7.8B</b></td>
<td>7.82</td><td><u>40.96</u></td><td>37.35</td><td><b>70.80</b></td><td><b>70.00</b></td><td><b>63.33</b></td><td><b>64.65</b></td><td>54.24</td><td>18.86</td><td><b>52.52</b></td><td>44.44</td>
</tr>
<tr>
<td><b>DeepSeek-R1-Distill-Qwen-7B</b></td>
<td>7.62</td><td>0.00</td><td>23.00</td><td>56.09</td><td>60.00</td><td>40.00</td><td>43.43</td><td>0.00</td><td>8.29</td><td>28.85</td><td>17.48</td>
</tr>
<tr>
<td><b>DeepSeek-R1-Distill-Llama-8B</b></td>
<td>8.03</td><td>23.22</td><td>26.26</td><td>29.97</td><td>33.33</td><td>20.00</td><td><U>46.46</u></td><td>39.05</td><td>13.29</td><td>28.95</td><td>26.36</td>
</tr>
<tr>
<td><b>s1.1-7B</b></td>
<td>7.62</td><td>31.16</td><td><u>37.70</u></td><td>30.60</td><td>16.67</td><td>23.33</td><td>30.30</td><td><u>56.84</u></td><td><u>21.86</u></td><td>31.06</td><td>35.63</td>
</tr>
<tr>
<td><b>OpenThinker3-7B</b></td>
<td>7.62</td><td>30.31</td><td>26.26</td><td><u>63.59</u></td><td><u>66.67</u></td><td><u>53.33</u></td><td><u>46.46</u></td><td>47.69</td><td>10.14</td><td>35.63</td><td>30.60</td>
</tr>
<tr>
<td><b>Ko-R1-7B</b></td>
<td>7.61</td><td>28.46</td><td>19.31</td><td>51.61</td><td>46.67</td><td>33.33</td><td>28.28</td><td>32.48</td><td>4.71</td><td>30.61</td><td>27.31</td>
</tr>
<tr>
<td><b>KO-REAson-KL3_1-8B-0831(KONI-Llama3.1-8B-R-20250831)</b></td>
<td>8.03</td><td>44.64</td><td>40.08</td><td>37.96</td><td>23.33</td><td>30.00</td><td>38.38</td><td>56.39</td><td>21.57</td><td>30.57</td><td>40.13</td>
</tr>
<tr>
<td><b>KO-REASon-AX3_1-7B-0831 (KONI-7B-R-20250831)</b></td>
<td>7.26</td><td>45.57</td><td>38.13</td><td>52.80</td><td>53.33</td><td>33.33</td><td>36.87</td><td><b>62.86</b></td><td>23.43</td><td><u>43.29</u></td><td><u>44.56</u></td>
</tr>
<tr>
<td><b>KO-REASon-7B-Q2_5-0831</b></td>
<td>7.26</td><td><b>46.81</b></td><td><b>44.93</b></td><td>48.11</td><td>43.33</td><td>30.00</td><td>42.93</td><td>60.65</td><td><b>25.00</b></td><td>42.72</td><td><b>45.10</b></td>
</tr>
</tbody>
</table>
## Citation
```
The paper will be released soon!
```
## Contact
For any questions contact us via the following email :)
```
spthsrbwls123@yonsei.ac.kr
```
## Acknowlegments
This research was supported by the Korea Institute of Science and Technology Information (KISTI) (No.(KISTI) K25L1M1C1), aimed at developing KONI (KISTI Open Neural Intelligence), a large language model specialized in science and technology.

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필요한 도구가 목록에 없는 경우에는 도구 호출 없이 사용자가 요구한 정보를 제공하세요.
필요한 도구가 목록에 있지만 해당 도구를 호출하는데 필요한 argument 정보가 부족한 경우 해당 정보를 사용자에게 요청하세요.
사용자의 요청을 처리하기 위해 여러번 도구를 호출할 수 있어야 합니다.
도구 호출 이후 도구 실행 결과를 입력으로 받으면 해당 결과를 활용하여 답변을 생성하세요.
다음은 접근할 수 있는 도구들의 목록 입니다:
<tools>
'}}
{%- for t in tools %}
{{- t | tojson }}
{{- '
' }}
{%- endfor %}
{{- '</tools>' }}
{{- '
도구를 호출하려면 아래의 JSON으로 응답하세요.
도구 호출 형식: <tool_call>{"name": 도구 이름, "arguments": dictionary 형태의 도구 인자값}</tool_call>' }}
{{- '<|im_end|>' }}
{%- endif %}
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{%- if message.role == 'system' %}
{{- '<|im_start|><|system|>' + message.content + '<|im_end|>'}}
{%- elif message.role == 'user' %}
{{- '<|im_start|><|user|>' + message.content + '<|im_end|>'}}
{%- elif message.role == 'assistant' %}
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{%- endif %}
{%- endfor %}
{%- if add_generation_prompt %}
{{- '<|im_start|><|assistant|>' }}
{%- endif %}

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"vocab_size": 102400
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