A.X K1 技术报告
计算与语言
2026-02-12 v5 人工智能
摘要
我们介绍了 A.X K1，一个从零开始训练的 519B 参数混合专家语言模型。我们的设计利用缩放定律，在固定计算预算下优化训练配置和词表大小。A.X K1 在由多阶段数据处理流水线精选的约 10T tokens 语料库上进行预训练。为弥合推理能力与推理效率之间的差距，A.X K1 支持显式可控的推理，以促进在多样化真实场景中的可扩展部署。我们提出了一种简单而有效的 Think-Fusion 训练方案，使得用户控制的在单一统一模型内的思考与非思考模式切换成为可能。广泛的评估表明，A.X K1 取得了与领先开源模型相竞争的性能，同时在韩语基准测试中确立了独特优势。
引用
@article{arxiv.2601.09200,
title = {A.X K1 Technical Report},
author = {Sung Jun Cheon and Jaekyung Cho and Seongho Choi and Hyunjun Eun and Seokhwan Jo and Jaehyun Jun and Minsoo Kang and Jin Kim and Jiwon Kim and Minsang Kim and Seungsik Kim and Sungwan Kim and Tae Yoon Kim and Youngrang Kim and Hyeongmun Lee and Sangyeol Lee and Sungeun Lee and Youngsoon Lee and Yujin Lee and Seongmin Ok and Chanyong Park and Hyewoong Park and Junyoung Park and Hyunho Yang and Subin Yi and Dhammiko Arya and Soohyun Bae and Dongyeon Cho and Seungmo Cho and Sangho Choi and Yongseok Choi and Gyoungeun Han and Yong-jin Han and Seokyoung Hong and Hyeon Hwang and Wonbeom Jang and Minjeong Ju and Wonjin Jung and Keummin Ka and Sungil Kang and Dongnam Kim and Jonghwi Kim and Joonghoon Kim and SaeRom Kim and Sangjin Kim and Seongwon Kim and Youngjin Kim and Seojin Lee and Sunwoo Lee and Taehoon Lee and Chanwoo Park and Sohee Park and Sooyeon Park and Yohan Ra and Sereimony Sek and Seungyeon Seo and Gun Song and Sanghoon Woo and Janghan Yoon and Sungbin Yoon},
journal= {arXiv preprint arXiv:2601.09200},
year = {2026}
}