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README.md
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# KO-REAson
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**KO-REAson** is a series of Korean-centric reasoning language models developed in collaboration with OneLineAI, KISTI, HAE-RAE and ORACLE.
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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.
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Top-performing models of our series [KO-REAson-AX3_1-7B-0831](https://huggingface.co/
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<p align="left">
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<img src="https://cdn-uploads.huggingface.co/production/uploads/60d3e619b8448e1785bbda2a/
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alt="Model Comparison" width="750"/>
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<br>
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<em style="display:inline-block; max-width:750px; text-align:cener; white-space:normal; word-wrap:break-word; line-height:1.5;">
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| Model (link) | Base | Notes |
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| -------------------------------------------------------------------------------------------- | -------------------- | --------------------------- |
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| [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 |
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| [KO-REAson-KL3_1-8B-0831](https://huggingface.co/
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| [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 |
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| [KO-REAson-AX3_1-7B-0831](https://huggingface.co/
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| [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 |
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| [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 |
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</thead>
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<tbody>
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<tr>
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<th>KO-REASon-AX3_1-7B-0831(Ours)</th>
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<td>260k</td>
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<td>SFT</td>
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<td><b>44.6</b></td>
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<td>38.75</td>
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<td><u>49.95</u></td>
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</tr>
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<tr>
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<td colspan="6" style="text-align:center; font-weight:bold;">Open Recipe (En)</td>
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</tr>
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<th>AIME 2024</th>
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<th>AIME 2025</th>
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<th>GPQA</th>
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<th>
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<th>KoBALT-700</th>
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</tr>
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</thead>
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<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>
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</tr>
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<tr>
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<td><b>KO-
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<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>
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</tr>
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<tr>
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# KO-REAson
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**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.
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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.
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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.
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<p align="left">
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<img src="https://cdn-uploads.huggingface.co/production/uploads/60d3e619b8448e1785bbda2a/uqrKdxbQEqAFknYBmuH7Y.png"
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alt="Model Comparison" width="750"/>
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<br>
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<em style="display:inline-block; max-width:750px; text-align:cener; white-space:normal; word-wrap:break-word; line-height:1.5;">
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| Model (link) | Base | Notes |
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| -------------------------------------------------------------------------------------------- | -------------------- | --------------------------- |
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| [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 |
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| [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) |
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| [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 |
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| [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) |
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| [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 |
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| [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 |
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</thead>
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<tbody>
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<tr>
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<th>KO-REASon-AX3_1-7B-0831(KONI-7B-R-20250831; Ours)</th>
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<td>260k</td>
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<td>SFT</td>
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<td><b>44.6</b></td>
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<td>38.75</td>
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<td><u>49.95</u></td>
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</tr>
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<tr>
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<th>KO-REAson-KL3_1-8B-0831(KONI-Llama3.1-8B-R-20250831)</th>
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<td>260k</td>
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<td>SFT</td>
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<td>40.13</td>
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<td>30.57</td>
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<td>43.66</td>
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</tr>
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<tr>
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<td colspan="6" style="text-align:center; font-weight:bold;">Open Recipe (En)</td>
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</tr>
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<th>AIME 2024</th>
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<th>AIME 2025</th>
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<th>GPQA</th>
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<th>CLIcK</th>
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<th>KoBALT-700</th>
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</tr>
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</thead>
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<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>
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</tr>
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<tr>
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<td><b>KO-REAson-KL3_1-8B-0831(KONI-Llama3.1-8B-R-20250831)</b></td>
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<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>
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</tr>
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<tr>
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<td><b>KO-REASon-AX3_1-7B-0831 (KONI-7B-R-20250831)</b></td>
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<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>
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</tr>
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<tr>
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