Solar Open Technical Report
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.
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Sungrae Park
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Sungrae Park
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Sanghoon Kim
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Jungho Cho
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Christopher Cho
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Gyoungjin Gim
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Dawoon Jung
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Mikyoung Cha
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Eunhae Choo
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Taekgyu Hong
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SeHwan Joo
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Minsoo Khang
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Eunwon Kim
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Yunsu Kim
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Yunsu Kim
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Hyeonju Lee
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Sukyung Lee
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Siyoung Park
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Gyungin Shin
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Inseo Song
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Inseo Song
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Wonho Song
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Wonho Song
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Seonghoon Yang
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Seungyoun Yi
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Kyle Yi
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Sanghoon Yoon
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Jeonghyun Ko
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Seyoung Song
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Seyoung Song
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Keunwoo Choi
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Keunwoo choi
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Hwalsuk Lee
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Du-Seong Chang
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Kyunghyun Cho
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Junsuk Choe
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KyungTae Lim
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Alice Oh
Solar Open Technical Report
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.
Solar Open presents a 102B-parameter bilingual Mixture-of-Experts language model that addresses data scarcity in underserved languages through synthetic data generation, progressive curriculum coordination, and scalable reinforcement learning optimization.
Mixture-of-Experts language model underserved languages data synthesis progressive curriculum reinforcement learning SnapPO domain-specific data quality thresholds composition optimization