Ling 2.0: Unlocking Trillion-Parameter Reasoning Through Efficient MoE Architectures
By Ling Team, Ang Li, Ben Liu, Binbin Hu, Bing Li, Bingwei Zeng, Borui Ye, Caizhi Tang, Changxin Tian, Chao Huang, Chao Zhang, Chen Qian, Chenchen Ju, Chenchen Li, Chengfu Tang, Chilin Fu, Chunshao Ren, Chunwei Wu, Cong Zhang, Cunyin Peng, Dafeng Xu, Daixin Wang, Dalong Zhang, Dingnan Jin, Dingyuan Zhu, Dongke Hu, Fangzheng Zhao, Feifan Wu, Feng Zhu, Gangshan Wang, Haitao Zhang, Hailin Zhao, Hanxiao Zhang, Hanzi Wang, Hao Qian, Haoyi Yu, Heng Zhang, Hongliang Zhang, Hongzhi Luan, Huirong Dong, Huizhong Li, Jia Li, Jia Liu, Jialong Zhu, Jian Sha, Jianping Wei, Jiaolong Yang, Jieyue Ma, Jiewei Wu, Jinjing Huang, Jingyun Tian, Jingyuan Zhang, Jinquan Sun, Juanhui Tu, Jun Liu, Jun Xu, Jun Zhou, Junjie Ou, Junpeng Fang, Kaihong Zhang, Kaiqin Hu, Ke Shi, Kun Tang, Kunlong Chen, Lanyin Mei, Lei Liang, Lei Xu, Libo Zhang, Lin Ju, Lin Yuan, Ling Zhong, Lintao Ma, Lu Liu, Lu Yu, Lun Cai, Meiqi Zhu, Mengying Li, Min Chen, Minghao Xue, Minghong Cai, Mingming Yin, Peijie Jiang, Peilong Zhao, Pingping Liu, Qian Zhao, Qing Cui, Qingxiang Huang, Qingyuan Yang, Quankun Yu, Shaowei Wei, Shijie Lian, Shoujian Zheng, Shun Song, Shungen Zhang, Shuo Zhang, Siyuan Li, Song Liu, Ting Guo, Tong Zhao, Wanli Gu, Weichang Wu, Weiguang Han, Wenjing Fang, Wubin Wang, Xiang Shu, Xiao Shi, Xiaoshun Lan, Xiaolu Zhang, Xiaqing Sun, Xin Zhao, Xingyu Lu, Xiong Xu, Xudong Wang, Xudong Wang, Xuemin Yang, Yajie Yang, Yang Xiang, Yanzhe Li, Yi Zhang, Yilong Wang, Yingxue Li, Yongzhen Guo, Yuzhuo Fu, Yuanyuan Wang, Yue Yang, Yue Yu, Yufeng Deng, Yun Zhang, Yunfei Yu, Yuqi Zhang, Yuxiao He, Zengke Gui, Zhaoxin Huan, Zhaoyang Wang, Zhibo Zhu, Zhihao Wang, Zhiqiang Zhang, Zhoufei Wang, Zihang Zeng, Ziqi Liu, Zitao Xuan, Zuoli Tang
Published on November 10, 2025| Vol. 1, Issue No. 1
Content Source
This is a curated briefing. The original article was published on cs.CL updates on arXiv.org.
Summary
Ling 2.0 introduces a new series of reasoning-oriented language foundation models, designed to scale from tens of billions up to one trillion parameters utilizing a unified Mixture-of-Experts (MoE) paradigm. This architecture emphasizes high sparsity and achieves significant active-compute efficiency, boasting up to 7-fold improvements compared to dense counterparts. The series, which includes non-thinking (instruct) models like Ling-mini-2.0, Ling-flash-2.0, and Ling-1T, integrates coordinated innovations across model architecture (high-sparsity MoE with MTP), pre-training (reasoning-oriented data, mid-training CoT activation), post-training (reinforcement-based fine-tuning), and infrastructure (full-scale FP8 training). Ling-1T, at the trillion-parameter scale, establishes a new Pareto frontier for reasoning accuracy and computational efficiency, demonstrating that sparse activation, when properly aligned with reasoning objectives, enables scalable and efficient intelligence and provides an open foundation for future reasoning models.
Why It Matters
This development is profoundly significant for the AI industry, marking a crucial evolution in how we approach the scaling of advanced language models. For AI professionals, Ling 2.0 highlights several critical trends. Firstly, it underscores the paramount importance of computational efficiency in the relentless pursuit of larger, more capable models. The reported 7-fold active-compute efficiency gain, coupled with the ability to scale to a trillion parameters, directly addresses the unsustainable energy and financial costs associated with traditional dense models. This breakthrough suggests that powerful AI can become more accessible, deployable, and sustainable for a wider array of applications and organizations. Secondly, Ling 2.0's explicit focus on "reasoning-oriented" design signals a maturation in model development. It indicates a strategic shift from simply generating coherent text to embedding and enhancing true reasoning capabilities, which are indispensable for tackling complex, high-value tasks across various industries. The integration of innovations from architecture to post-training methods demonstrates a holistic approach to building intelligence, rather than relying solely on raw data volume or parameter count. Finally, by pushing the Pareto frontier of reasoning accuracy versus computational efficiency, Ling 2.0 provides a powerful blueprint for future AI systems. It unequivocally demonstrates that advanced intelligence doesn't have to come at an exorbitant price, paving the way for more responsible, sustainable, and impactful AI development that could accelerate both research and real-world deployment of truly intelligent agents.