Every Activation Boosted: Scaling General Reasoner to 1 Trillion Open
  Language Foundation
We introduce Ling 2.0, a series reasoning-oriented language foundation built
upon the principle that every activation boosts reasoning capability. Designed
to scale from tens of billions to one trillion parameters under a unified
Mixture-of-Experts (MoE) paradigm, Ling 2.0 emphasizes high sparsity,
cross-scale consistency, and efficiency guided by empirical scaling laws. The
series includes three non-thinking (instruct) models - Ling-mini-2.0,
Ling-flash-2.0, and Ling-1T - ranging from 16B to 1T total parameters and
achieving up to 7-fold active-compute efficiency compared with dense
counterparts. Ling 2.0 integrates coordinated innovations across model
architecture, pre-training, post-training, and infrastructure: a high-sparsity
MoE with MTP for efficient reasoning, reasoning-oriented data and mid-training
CoT activation, reinforcement-based fine-tuning (DFT, Evo-CoT), and full-scale
FP8 training with fine-grained heterogeneous pipelines. At the trillion scale,
Ling-1T establishes a new Pareto frontier of reasoning accuracy versus
computational efficiency, demonstrating that sparse activation, when properly
aligned with reasoning objectives, enables scalable and efficient intelligence.
Collectively, Ling 2.0 provides a coherent, open, and efficient foundation for
advancing future reasoning and thinking models, including the Ring series built
upon the same base.
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Ling-Team
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Ang Li
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Ang Li
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Ben Liu
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Binbin Hu
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Bing Li
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Bingwei Zeng
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Borui Ye
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Borui Ye
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Caizhi Tang
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Changxin Tian
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Changxin Tian
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Chao Huang
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Chao Zhang
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Chen Qian
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Chenchen Ju
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Chenchen Li
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Chengfu Tang
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Chili Fu
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Chunshao Ren
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Chunwei Wu
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Cong Zhang
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Cunyin Peng
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Cunyin Peng
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Dafeng Xu
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Daixin Wang
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Dalong Zhang
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Dingnan Jin
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Dingyuan Zhu
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Dongke Hu
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Fangzheng Zhao
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Feifan Wu
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Feifan Wu
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Feng Zhu
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Gangshan Wang
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Haitao Zhang
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Hailin Zhao
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hailin
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Hanxiao Zhang
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zhanghanxiao
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Hanzi Wang
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Hongzhi Luan
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Huirong Dong
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Jia Li
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Jia Liu
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Jialong Zhu
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Jian Sha
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Jianping Wei
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Jieyue Ma
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Jiewei Wu
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Jinjing Huang
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Jinquan Sun
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Juanhui Tu
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Jun Liu
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Jun Xu
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Lu Liu
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liulu
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Lu Yu
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Lun Cai
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Meiqi Zhu
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MeiqiZhu
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Zuoli Tang
Every Activation Boosted: Scaling General Reasoner to 1 Trillion Open
  Language Foundation
We introduce Ling 2.0, a series reasoning-oriented language foundation built
upon the principle that every activation boosts reasoning capability. Designed
to scale from tens of billions to one trillion parameters under a unified
Mixture-of-Experts (MoE) paradigm, Ling 2.0 emphasizes high sparsity,
cross-scale consistency, and efficiency guided by empirical scaling laws. The
series includes three non-thinking (instruct) models - Ling-mini-2.0,
Ling-flash-2.0, and Ling-1T - ranging from 16B to 1T total parameters and
achieving up to 7-fold active-compute efficiency compared with dense
counterparts. Ling 2.0 integrates coordinated innovations across model
architecture, pre-training, post-training, and infrastructure: a high-sparsity
MoE with MTP for efficient reasoning, reasoning-oriented data and mid-training
CoT activation, reinforcement-based fine-tuning (DFT, Evo-CoT), and full-scale
FP8 training with fine-grained heterogeneous pipelines. At the trillion scale,
Ling-1T establishes a new Pareto frontier of reasoning accuracy versus
computational efficiency, demonstrating that sparse activation, when properly
aligned with reasoning objectives, enables scalable and efficient intelligence.
Collectively, Ling 2.0 provides a coherent, open, and efficient foundation for
advancing future reasoning and thinking models, including the Ring series built
upon the same base.
Ling 2.0, a reasoning-oriented language model series, achieves high efficiency and accuracy through a Mixture-of-Experts paradigm, sparse activation, and innovative training techniques.
Mixture-of-Experts (MoE) high sparsity cross-scale consistency MTP reasoning-oriented data mid-training CoT activation reinforcement-based fine-tuning (DFT Evo-CoT) full-scale FP8 training fine-grained heterogeneous pipelines sparse activation reasoning accuracy computational efficiency