{"slug": "ling-and-ring-2-6-technical-report-efficient-and-instant-agentic-intelligence-at", "title": "Ling and Ring 2.6 Technical Report: Efficient and Instant Agentic Intelligence at Trillion-Parameter Scale", "summary": "Researchers introduced Ling-2.6 and Ring-2.6, a family of models designed for efficient and scalable agentic intelligence at trillion-parameter scale. Ling-2.6 optimizes for instant response generation, while Ring-2.6 focuses on deeper reasoning and advanced agentic workflows, achieved through architectural migration, hybrid linear attention, and a reinforcement learning framework called KPop. The models are open-sourced to support further research in practical agentic systems.", "body_md": "arXiv:2606.15079v1 Announce Type: new\nAbstract: 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.", "url": "https://wpnews.pro/news/ling-and-ring-2-6-technical-report-efficient-and-instant-agentic-intelligence-at", "canonical_source": "https://arxiv.org/abs/2606.15079", "published_at": "2026-06-16 04:00:00+00:00", "updated_at": "2026-06-16 04:23:28.495038+00:00", "lang": "en", "topics": ["large-language-models", "ai-agents", "ai-research", "ai-infrastructure", "ai-products"], "entities": ["Ling-2.6", "Ring-2.6", "Ling-2.0", "KPop", "Lightning Attention", "MLA", "arXiv"], "alternates": {"html": "https://wpnews.pro/news/ling-and-ring-2-6-technical-report-efficient-and-instant-agentic-intelligence-at", "markdown": "https://wpnews.pro/news/ling-and-ring-2-6-technical-report-efficient-and-instant-agentic-intelligence-at.md", "text": "https://wpnews.pro/news/ling-and-ring-2-6-technical-report-efficient-and-instant-agentic-intelligence-at.txt", "jsonld": "https://wpnews.pro/news/ling-and-ring-2-6-technical-report-efficient-and-instant-agentic-intelligence-at.jsonld"}}