Core Insights - Ant Group has officially launched the trillion-parameter thinking model Ring-1T, which is fully open-sourced including model weights and training recipes [2] - Ring-1T has shown significant improvements in natural language reasoning capabilities and general performance across various tasks compared to its preview version [2] - The model achieved impressive results in the International Mathematical Olympiad (IMO) challenges, demonstrating its ability to solve complex mathematical problems [2] Model Performance - Ring-1T achieved a success rate of 81.59% in the Arena-Hard V2 human preference alignment test, ranking first among open-source models and closely approaching the performance of GPT-5-Thinking (High) at 82.91% [3] - In the HealthBench evaluation for medical Q&A, Ring-1T also scored the highest, marking it as the best in the open-source domain [3] Technical Innovations - Ant Group addressed the challenge of training and inference precision discrepancies in trillion-parameter models by developing the "icepop" algorithm, which stabilizes the training-inference distribution [5] - The company also created a high-performance reinforcement learning system called ASystem, optimizing memory management and weight exchange for large-scale RL training [6] Model Architecture - Ring-1T continues to utilize the Ling 2.0 architecture, which incorporates features like highly sparse MoE architecture and mixed precision training to enhance efficiency [8] - The model underwent multi-stage training processes, including LongCoT-SFT, RLVR, and RLHF, significantly improving its complex reasoning and general capabilities [8] Product Matrix - Ant Group has released a total of 18 models, ranging from 16 billion to 1 trillion parameters, marking the transition of its large language model products into the 2.0 phase with the introduction of Ring-1T and Ling-1T [9]