inclusionAI/Ring-2.6-1T · Hugging Face

Introducing Ring-2.6-1T: a trillion-parameter flagship reasoning model designed for real-world complex task scenarios, making it available to developers, researchers, and enterprise environments for validation, adaptation, and further development.

The goal of Ring-2.6-1T is not simply to pursue larger parameter scale , but to address the real production environments that large models are entering: agent workflows, engineering development, scientific research analysis, complex business systems, and enterprise automation processes. In these scenarios, models need not only to "answer questions," but also to understand context, plan steps, invoke tools, execute continuously, and maintain stability over long-horizon tasks.

Ring-2.6-1T has achieved key upgrade in three areas:

• Comprehensively enhanced Agent execution capability: Moving from "being able to answer" to "being able to execute," with more stable performance in multi-step tasks, tool collaboration, contextual planning, and advancing complex workflows.
• Reasoning Effort mechanism: Supporting two reasoning intensity levels, high and xhigh, allowing developers to flexibly adjust the depth of thinking according to task complexity, achieving a better balance among effectiveness, speed, and cost.
• Innovative asynchronous reinforcement learning training paradigm: Leveraging an Async RL architecture combined with the IcePop algorithm to improve the training efficiency and stability of long-horizon reinforcement learning for trillion-parameter models, providing foundational support for agent capabilities and complex reasoning.