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
Published: 2025-10-21 17:46:14

Abstract: 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.