Every Step Evolves: Scaling Reinforcement Learning for Trillion-Scale
  Thinking Model
Authors: name: Ling Team, name: Anqi Shen, name: Baihui Li, name: Bin Hu, name: Bin Jing, name: Cai Chen, name: Chao Huang, name: Chao Zhang, name: Chaokun Yang, name: Cheng Lin, name: Chengyao Wen, name: Congqi Li, name: Deng Zhao, name: Dingbo Yuan, name: Donghai You, name: Fagui Mao, name: Fanzhuang Meng, name: Feng Xu, name: Guojie Li, name: Guowei Wang, name: Hao Dai, name: Haonan Zheng, name: Hong Liu, name: Jia Guo, name: Jiaming Liu, name: Jian Liu, name: Jianhao Fu, name: Jiannan Shi, name: Jianwen Wang, name: Jianxin Lai, name: Jin Yang, name: Jun Mei, name: Jun Zhou, name: Junbo Zhao, name: Junping Zhao, name: Kuan Xu, name: Le Su, name: Lei Chen, name: Li Tang, name: Liang Jiang, name: Liangcheng Fu, name: Lianhao Xu, name: Linfeng Shi, name: Lisha Liao, name: Longfei Zheng, name: Meng Li, name: Mingchun Chen, name: Qi Zuo, name: Qiang Cheng, name: Qianggang Cao, name: Qitao Shi, name: Quanrui Guo, name: Senlin Zhu, name: Shaofei Wang, name: Shaomian Zheng, name: Shuaicheng Li, name: Shuwei Gu, name: Siba Chen, name: Tao Wu, name: Tao Zhang, name: Tianyu Zhang, name: Tianyu Zhou, name: Tiwei Bie, name: Tongkai Yang, name: Wang Hong, name: Wang Ren, name: Weihua Chen, name: Wenbo Yu, name: Wengang Zheng, name: Xiangchun Wang, name: Xiaodong Yan, name: Xiaopei Wan, name: Xin Zhao, name: Xinyu Kong, name: Xinyu Tang, name: Xudong Han, name: Xudong Wang, name: Xuemin Yang, name: Xueyu Hu, name: Yalin Zhang, name: Yan Sun, name: Yicheng Shan, name: Yilong Wang, name: Yingying Xu, name: Yongkang Liu, name: Yongzhen Guo, name: Yuanyuan Wang, name: Yuchen Yan, name: Yuefan Wang, name: Yuhong Guo, name: Zehuan Li, name: Zhankai Xu, name: Zhe Li, name: Zhenduo Zhang, name: Zhengke Gui, name: Zhenxuan Pan, name: Zhenyu Huang, name: Zhenzhong Lan, name: Zhiqiang Ding, name: Zhiqiang Zhang, name: Zhixun Li, name: Zhizhen Liu, name: Zihao Wang, name: Zujie Wen
Organization: inclusionAI

We present Ring-1T, the first open-source, state-of-the-art thinking model
with a trillion-scale parameter. It features 1 trillion total parameters and
activates approximately 50 billion per token. Training such models at a
trillion-parameter scale introduces unprecedented challenges, including
train-inference misalignment, inefficiencies in rollout processing, and
bottlenecks in the RL system. To address these, we pioneer three interconnected
innovations: (1) IcePop stabilizes RL training via token-level discrepancy
masking and clipping, resolving instability from training-inference mismatches;
(2) C3PO++ improves resource utilization for long rollouts under a token budget
by dynamically partitioning them, thereby obtaining high time efficiency; and
(3) ASystem, a high-performance RL framework designed to overcome the systemic
bottlenecks that impede trillion-parameter model training. Ring-1T delivers
breakthrough results across critical benchmarks: 93.4 on AIME-2025, 86.72 on
HMMT-2025, 2088 on CodeForces, and 55.94 on ARC-AGI-v1. Notably, it attains a
silver medal-level result on the IMO-2025, underscoring its exceptional
reasoning capabilities. By releasing the complete 1T parameter MoE model to the
community, we provide the research community with direct access to cutting-edge
reasoning capabilities. This contribution marks a significant milestone in
democratizing large-scale reasoning intelligence and establishes a new baseline
for open-source model performance.