K-EXAONE 2.0 Technical Report
Authors: name: Eunbi Choi, name: Kibong Choi, name: Sehyun Chun, name: Seokhee Hong, name: Junwon Hwang, name: Hyojin Jeon, name: Ahra Jo, name: Hyunjik Jo, name: Yeonsik Jo, name: Minhyeok Jung, name: Doyoung Kim, name: Heegyu Kim, name: Joonkee Kim, name: Seonghwan Kim, name: Soyeon Kim, name: Sunkyoung Kim, name: Yireun Kim, name: Yongil Kim, name: Byungoh Ko, name: Changhun Lee, name: Dohaeng Lee, name: Haeju Lee, name: Jinsik Lee, name: Kyungmin Lee, name: Minwoo Lee, name: Wonkee Lee, name: Sangha Park, name: Sungjune Park, name: Kwangrok Ryoo, name: Kijung Seo, name: Minju Seo, name: Yongwoo Song, name: Sejong Yang, name: Heuiyeen Yeen, name: Stanley Jungkyu Choi, name: Yemuk Choi, name: Yongchan Chun, name: Jiwon Ham, name: Dasol Hong, name: Sujeong Im, name: Kijeong Jeon, name: Gerrard Jeongwon Jo, name: Hyeongjun Jo, name: Yujin Jo, name: Jiyeon Jung, name: Naeun Kang, name: Daeseong Kim, name: Euisoon Kim, name: Hayeon Kim, name: Hyosang Kim, name: Myoungshin Kim, name: Unsol Kim, name: Youchul Kim, name: Chaeeun Lee, name: ChaeYoon Lee, name: Edward Hwayoung Lee, name: Honglak Lee, name: Hwansoo Lee, name: Minkyung Lee, name: Sangeun Lee, name: Solji Lim, name: Woohyung Lim, name: Chanwoo Moon, name: Jueun Mun, name: Jimin Park, name: Seojeong Park, name: Yongmin Park, name: Hyerin Seo, name: Donghyeon Shin, name: Donghyun Son, name: Eunyong Son, name: Kaehyun Um, name: Sihoon Yang, name: Chang En Yea, name: Sihyuk Yi, name: Kyungjae Yoo, name: Chansik Yoon
Organization: LG AI Research

This technical report presents K-EXAONE 2.0, an open-weight multilingual foundation model developed by LG AI Research as a step in our effort toward global frontier-scale foundation models. Rather than training from scratch, we upcycle K-EXAONE and expand its architecture, yielding a Mixture-of-Experts (MoE) model with 750B total parameters and approximately 37B activated per token---more than three times the capacity of its predecessor. K-EXAONE 2.0 supports context lengths of up to 256K tokens and expands multilingual coverage from six to ten languages. Its training pipeline combines continual pre-training, difficulty-focused mid-training, and post-training to strengthen reasoning, agentic coding, multilingual capability, and safety grounded in Korean sociocultural contexts. Across nine evaluation categories selected to reflect the conditions of practical use, K-EXAONE 2.0 improves over K-EXAONE and remains competitive with open-weight models, showing its largest gains in agentic coding and long-context understanding and its clearest strengths in long-context retrieval and safety. Released under the Apache 2.0 license, K-EXAONE 2.0 enables the wider AI ecosystem to evaluate, deploy, adapt, and build upon it, while marking the beginning---rather than the endpoint---of our challenge toward the global frontier.