Ring-linear: Efficient Hybrid Architecture for Long-Context Reasoning

This October 23, 2025 technical report from the Ling Team introduces the **Ring-linear model series**, specifically Ring-mini-linear-2.0 and Ring-flash-linear-2.0, which utilize a **hybrid attention architecture** combining linear and softmax attention mechanisms to enhance efficiency in long-context reasoning. The paper explains how this architecture, featuring **Mixture-of-Experts (MoE)** and advanced **FP8 training optimization** through kernels like LingHe, significantly reduces inference costs and improves training throughput. A major focus is on **systematic training-inference alignment** to achieve stable reinforcement learning (RL) training, addressing disparities in components like the KV Cache and RMSNorm that often lead to RL collapse in long-context models. Finally, the report presents **benchmark results** demonstrating that the Ring-linear models maintain state-of-the-art performance across various complex reasoning tasks compared to similar-scale counterparts.

Source:

https://arxiv.org/pdf/2510.19338