---
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.