Every Attention Matters: An Efficient Hybrid Architecture for
  Long-Context Reasoning
Authors: name: Ling Team, name: Bin Han, name: Caizhi Tang, name: Chen Liang, name: Donghao Zhang, name: Fan Yuan, name: Feng Zhu, name: Jie Gao, name: Jingyu Hu, name: Longfei Li, name: Meng Li, name: Mingyang Zhang, name: Peijie Jiang, name: Peng Jiao, name: Qian Zhao, name: Qingyuan Yang, name: Wenbo Shen, name: Xinxing Yang, name: Yalin Zhang, name: Yankun Ren, name: Yao Zhao, name: Yibo Cao, name: Yixuan Sun, name: Yue Zhang, name: Yuchen Fang, name: Zibin Lin, name: Zixuan Cheng, name: Jun Zhou
Organization: Ant Group

In this technical report, we present the Ring-linear model series,
specifically including Ring-mini-linear-2.0 and Ring-flash-linear-2.0.
Ring-mini-linear-2.0 comprises 16B parameters and 957M activations, while
Ring-flash-linear-2.0 contains 104B parameters and 6.1B activations. Both
models adopt a hybrid architecture that effectively integrates linear attention
and softmax attention, significantly reducing I/O and computational overhead in
long-context inference scenarios. Compared to a 32 billion parameter dense
model, this series reduces inference cost to 1/10, and compared to the original
Ring series, the cost is also reduced by over 50%. Furthermore, through
systematic exploration of the ratio between different attention mechanisms in
the hybrid architecture, we have identified the currently optimal model
structure. Additionally, by leveraging our self-developed high-performance FP8
operator library-linghe, overall training efficiency has been improved by 50%.
Benefiting from the high alignment between the training and inference engine
operators, the models can undergo long-term, stable, and highly efficient
optimization during the reinforcement learning phase, consistently maintaining
SOTA performance across multiple challenging complex reasoning benchmarks.