Bailin Large Model officially announced the open source of its trillion-parameter flagship reasoning model Ring-2.6-1T today, aiming to address the issue of "insufficient execution capability" of large models in real production environments. This model is not just a stacking of parameter scale, but also a core shift towards end-to-end promotion of long-chain tasks such as Agent workflows, software engineering, and scientific analysis.

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On the technical level, Ring-2.6-1T has achieved three core breakthroughs: First, it significantly enhances Agent execution capabilities, achieving open-source SOTA levels in benchmark tests such as PinchBench and ClawEval that evaluate Agent adaptability, greatly optimizing task decomposition and feedback correction capabilities;

Second, it introduces an innovative "Reasoning Effort" adjustable mechanism, supporting two levels of reasoning intensity: high and xhigh, allowing developers to balance cost and performance based on task complexity. The high level performs well in Tau2-Bench telecom business tests, while the xhigh level reaches the capability limit in high-difficulty reasoning tasks such as AIME26 and GPQA Diamond;

Finally, the model adopts an asynchronous (Async) reinforcement learning architecture combined with the "Ice Stick Algorithm," effectively solving the stability issues of long-period training for trillion-scale models and significantly improving resource utilization.

Ring-2.6-1T is now available on Hugging Face and ModelScope. Although the team acknowledges there is still room for improvement in Long-Horizon long-term delivery stability, the open source of this model marks a qualitative change in AI from single dialogue interaction to an execution engine with autonomous planning and tool collaboration capabilities, providing global developers with a foundational support for exploring complex automation processes.