LLM-jp-4 Technical Report

LLM-jp; :; Chaoran Liu; Daisuke Kawahara; Hideyuki Tachibana; Hirokazu Kiyomaru; Jun Suzuki; Koshi Eguchi; Kouta Nakayama; Qianying Liu; Rio Yokota; Ryo Takahashi; Sadao Kurohashi; Sosuke Hosokawa; Taishi Nakamura; Takashi Kodama; Yun-Ang Wu; Yusuke Miyao; Yusuke Oda

arXiv · 2610.19331

LLM-jp-4 Technical Report

LLM-jp·:·Chaoran Liu·Daisuke Kawahara·Hideyuki Tachibana·Hirokazu Kiyomaru·Jun Suzuki·Koshi Eguchi·Kouta Nakayama·Qianying Liu·Rio Yokota·Ryo Takahashi·Sadao Kurohashi·Sosuke Hosokawa·Taishi Nakamura·Takashi Kodama·Yun-Ang Wu·Yusuke Miyao·Yusuke Oda

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Abstract

This paper presents LLM-jp-4, a family of Japanese sovereign large language models (LLMs) developed with an emphasis on transparency, reproducibility, and practical usability. LLM-jp-4 comprises an 8B dense model and a 32B-A3B mixture-of-experts model, both trained from scratch on approximately 12 trillion tokens. To support independent verification and reproduction, we establish explicit criteria for selecting training data, external models, and software, and make the development process publicly available. Evaluation results demonstrate substantial improvements over previous LLM-jp models and competitive performance against open-weight models of similar scale. By documenting the development principles, training procedures, and empirical findings of LLM-jp-4, we aim to provide a reference for future sovereign AI development.

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LLM-jp, :, Chaoran Liu, Daisuke Kawahara, Hideyuki Tachibana, Hirokazu Kiyomaru, Jun Suzuki, Koshi Eguchi, Kouta Nakayama, Qianying Liu, Rio Yokota, Ryo Takahashi, Sadao Kurohashi, Sosuke Hosokawa, Taishi Nakamura, Takashi Kodama, Yun-Ang Wu, Yusuke Miyao, Yusuke Oda. 2026-10-14. LLM-jp-4 Technical Report. https://arxiv.org/abs/2610.19331

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