Gamayun's Path to Multilingual Mastery: Cost-Efficient Training of a 1.5B-Parameter LLM

• URL: http://arxiv.org/abs/2512.21580v2
• Date: Mon, 29 Dec 2025 11:27:46 GMT
• Title: Gamayun's Path to Multilingual Mastery: Cost-Efficient Training of a 1.5B-Parameter LLM
• Authors: Alexander Podolskiy, Semen Molokov, Timofey Gerasin, Maksim Titov, Alexey Rukhovich, Artem Khrapov, Kirill Morozov, Evgeny Tetin, Constantine Korikov, Pavel Efimov, Polina Lazukova, Yuliya Skripkar, Nikita Okhotnikov, Irina Piontkovskaya, Meng Xiaojun, Zou Xueyi, Zhang Zhenhe,
• Abstract summary: We present Gamayun, a multilingual language model trained entirely from scratch on 2.5T tokens.<n>Our model supports 12 languages, with special focus on Russian.<n>It matches or exceeds Qwen3 (36T tokens) on most tasks outside advanced STEM.
• Score: 30.381516759139203
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We present Gamayun, a 1.5B-parameter multilingual language model trained entirely from scratch on 2.5T tokens. Designed for efficiency and deployment in resource-constrained environments, Gamayun addresses the lack of research on small non-English-centric LLMs by adopting a novel two-stage pre-training strategy: balanced multilingual training for cross-lingual alignment, followed by high-quality English enrichment to transfer performance gains across languages. Our model supports 12 languages, with special focus on Russian. Despite a significantly smaller training budget than comparable models, Gamayun outperforms LLaMA3.2-1B (9T tokens) on all considered benchmarks, and surpasses Qwen2.5-1.5B (18T tokens) on a wide range of English and multilingual tasks. It matches or exceeds Qwen3 (36T tokens) on most tasks outside advanced STEM, achieving state-of-the-art results in Russian, including the MERA benchmark, among the models of comparable size (1-2B parameters).

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