Every Step Evolves: Scaling Reinforcement Learning for Trillion-Scale
  Thinking Model
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.
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Ling Team
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Anqi Shen
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Baihui Li
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Bin Hu
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Bin Jing
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Cai Chen
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Chao Huang
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Chao Zhang
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Chaokun Yang
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Cheng Lin
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Chengyao Wen
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Congqi Li
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Deng Zhao
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Dingbo Yuan
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Donghai You
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Fagui Mao
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Fanzhuang Meng
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Feng Xu
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Guojie Li
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Guowei Wang
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Hao Dai
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Haonan Zheng
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Hong Liu
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Jia Guo
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Jiaming Liu
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Jian Liu
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Jiannan Shi
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Jianwen Wang
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Junping Zhao
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Kuan Xu
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Le Su
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Li Tang
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Lisha Liao
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Meng Li
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Qi Zuo
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Qiang Cheng
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Shuwei Gu
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Tao Wu
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Every Step Evolves: Scaling Reinforcement Learning for Trillion-Scale
  Thinking Model
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.
Ring-1T, a trillion-parameter open-source thinking model, addresses training challenges with IcePop, C3PO++, and ASystem, achieving top results across benchmarks and democratizing large-scale reasoning intelligence.
IcePop C3PO++ ASystem MoE token-level discrepancy masking clipping resource utilization rollout processing RL system training-inference misalignment trillion-parameter model AIME-2025 HMMT-2025 CodeForces ARC-AGI-v1 IMO-2025