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| Main Authors: | , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2601.07022 |
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| _version_ | 1866912817340219392 |
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| author | Park, Sungrae Kim, Sanghoon Cho, Jungho Gim, Gyoungjin Jung, Dawoon Cha, Mikyoung Choo, Eunhae Hong, Taekgyu Jeong, Minbyul Joo, SeHwan Khang, Minsoo Kim, Eunwon Kim, Minjeong Kim, Sujeong Kim, Yunsu Lee, Hyeonju Lee, Seunghyun Lee, Sukyung Park, Siyoung Shin, Gyungin Song, Inseo Song, Wonho Yang, Seonghoon Yi, Seungyoun Yoon, Sanghoon Ko, Jeonghyun Song, Seyoung Choi, Keunwoo Lee, Hwalsuk Kim, Sunghun Chang, Du-Seong Cho, Kyunghyun Choe, Junsuk Lee, Hwaran Lee, Jae-Gil Lim, KyungTae Oh, Alice |
| author_facet | Park, Sungrae Kim, Sanghoon Cho, Jungho Gim, Gyoungjin Jung, Dawoon Cha, Mikyoung Choo, Eunhae Hong, Taekgyu Jeong, Minbyul Joo, SeHwan Khang, Minsoo Kim, Eunwon Kim, Minjeong Kim, Sujeong Kim, Yunsu Lee, Hyeonju Lee, Seunghyun Lee, Sukyung Park, Siyoung Shin, Gyungin Song, Inseo Song, Wonho Yang, Seonghoon Yi, Seungyoun Yoon, Sanghoon Ko, Jeonghyun Song, Seyoung Choi, Keunwoo Lee, Hwalsuk Kim, Sunghun Chang, Du-Seong Cho, Kyunghyun Choe, Junsuk Lee, Hwaran Lee, Jae-Gil Lim, KyungTae Oh, Alice |
| contents | We introduce Solar Open, a 102B-parameter bilingual Mixture-of-Experts language model for underserved languages. Solar Open demonstrates a systematic methodology for building competitive LLMs by addressing three interconnected challenges. First, to train effectively despite data scarcity for underserved languages, we synthesize 4.5T tokens of high-quality, domain-specific, and RL-oriented data. Second, we coordinate this data through a progressive curriculum jointly optimizing composition, quality thresholds, and domain coverage across 20 trillion tokens. Third, to enable reasoning capabilities through scalable RL, we apply our proposed framework SnapPO for efficient optimization. Across benchmarks in English and Korean, Solar Open achieves competitive performance, demonstrating the effectiveness of this methodology for underserved language AI development. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_07022 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Solar Open Technical Report Park, Sungrae Kim, Sanghoon Cho, Jungho Gim, Gyoungjin Jung, Dawoon Cha, Mikyoung Choo, Eunhae Hong, Taekgyu Jeong, Minbyul Joo, SeHwan Khang, Minsoo Kim, Eunwon Kim, Minjeong Kim, Sujeong Kim, Yunsu Lee, Hyeonju Lee, Seunghyun Lee, Sukyung Park, Siyoung Shin, Gyungin Song, Inseo Song, Wonho Yang, Seonghoon Yi, Seungyoun Yoon, Sanghoon Ko, Jeonghyun Song, Seyoung Choi, Keunwoo Lee, Hwalsuk Kim, Sunghun Chang, Du-Seong Cho, Kyunghyun Choe, Junsuk Lee, Hwaran Lee, Jae-Gil Lim, KyungTae Oh, Alice Computation and Language We introduce Solar Open, a 102B-parameter bilingual Mixture-of-Experts language model for underserved languages. Solar Open demonstrates a systematic methodology for building competitive LLMs by addressing three interconnected challenges. First, to train effectively despite data scarcity for underserved languages, we synthesize 4.5T tokens of high-quality, domain-specific, and RL-oriented data. Second, we coordinate this data through a progressive curriculum jointly optimizing composition, quality thresholds, and domain coverage across 20 trillion tokens. Third, to enable reasoning capabilities through scalable RL, we apply our proposed framework SnapPO for efficient optimization. Across benchmarks in English and Korean, Solar Open achieves competitive performance, demonstrating the effectiveness of this methodology for underserved language AI development. |
| title | Solar Open Technical Report |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2601.07022 |