Efficient and scalable agentic intelligence requires models that can deliver both low-latency responses and strong reasoning capabilities while remaining practical to train, serve, and deploy. In this report, we present Ling-2.6 and Ring-2.6, a family of models designed to address this challenge at scale. Ling-2.6 is optimized for instant response generation and high capability per output token, whereas Ring-2.6 is tailored for deeper reasoning and more advanced agentic workflows. Instead of training from scratch, we upgrade the Ling-2.0 base model through architectural migration pre-training and large-scale post-training. This upgrade is guided by a unified co-design of model architecture, optimization objectives, serving systems, and agent training environments, enabling improvements in both model capability and deployment efficiency. At the architectural level, we introduce a hybrid linear attention design that integrates Lightning Attention with MLA, improving the efficiency of long-context training and decoding. To further enhance token efficiency, we optimize capability per output token through Evolutionary Chain-of-Thought, Linguistic Unit Policy Optimization, bidirectional preference alignment, and shortest-correct-response distillation. For agentic capabilities, we propose KPop, a reinforcement learning framework designed to support stable training of Ring-2.6-1T on large-scale environment-grounded data. KPop improves training efficiency through asynchronous scheduling across coding, search, tool use, and workflow execution, enabling scalable learning from complex agent-environment interactions. Together, Ling-2.6 and Ring-2.6 provide a practical pathway toward efficient, scalable, and open agentic systems. We open-source all checkpoints in the 2.6 family to support further research and development in practical agentic intelligence.

翻译：高效且可扩展的智能体智能，需要模型既能提供低延迟响应，又能具备强大的推理能力，同时在实际训练、服务和部署中保持可行性。本报告介绍 Ling-2.6 和 Ring-2.6 系列模型，该系列专为应对这一规模化挑战而设计。Ling-2.6 针对即时响应生成和单位输出token的高能力进行优化；而 Ring-2.6 则专注于更深层次的推理和更高级的智能体工作流。我们并非从零开始训练，而是通过架构迁移预训练和大规模后训练，对 Ling-2.0 基础模型进行升级。此次升级以模型架构、优化目标、服务系统和智能体训练环境的统一协同设计为指导，在模型能力与部署效率两方面实现同步提升。在架构层面，我们引入了一种融合 Lightning Attention 与 MLA 的混合线性注意力设计，提升了长上下文训练与解码的效率。为进一步提升 token 效率，我们通过演化思维链、语言单元策略优化、双向偏好对齐以及最短正确响应蒸馏技术，优化了每个输出 token 的能力。为增强智能体能力，我们提出了 KPop，一种旨在支持 Ring-2.6-1T 在大规模环境接地数据上稳定训练的强化学习框架。KPop 通过编码、搜索、工具使用和工作流执行之间的异步调度，提升了训练效率，实现了从复杂智能体-环境交互中学习的能力可扩展性。Ling-2.6 与 Ring-2.6 共同为构建高效、可扩展且开源的智能体系统提供了实用路径。我们开源了 2.6 系列的所有检查点，以支持实用智能体智能领域的进一步研究与发展。