HomeBai Ling Large Model Open Sources Ring-2.6-1T, Targeting Real Complex Task Loops
Bailin Large Model today officially open-sourced its trillion-parameter flagship reasoning model, Ring-2.6-1T , designed to address the challenge of large models' limited execution capabilities in real-world production environments. This model goes beyond simply scaling up parameters; it marks a fundamental shift toward end-to-end advancement of long-chain tasks such as agent workflows, software engineering, and scientific analysis.
On the technical front, Ring-2.6-1T achieves three key breakthroughs. First, it significantly boosts agent execution capabilities, reaching open-source state-of-the-art results on benchmarks like PinchBench and ClawEval that evaluate agent adaptability, while greatly improving task decomposition and feedback correction.
Second, it introduces an innovative adjustable mechanism called "Reasoning Effort," with two intensity levels: high and xhigh. This lets developers balance cost and performance based on task complexity. The high level delivers strong results on Tau2-Bench telecom business tests, while the xhigh level reaches the capability ceiling on high-difficulty reasoning tasks such as AIME26 and GPQA Diamond.
Finally, the model adopts an asynchronous reinforcement learning architecture combined with the "Ice Stick Algorithm," effectively resolving stability issues during long training cycles for trillion-parameter models and significantly improving resource utilization.
Ring-2.6-1T is now available on Hugging Face and ModelScope . While the team acknowledges room for improvement in long-horizon delivery stability, the open-sourcing of this model represents a qualitative shift for AI—from single dialogue interactions to an execution engine with autonomous planning and tool collaboration capabilities—providing global developers with a solid foundation for exploring complex automation processes.
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On the technical front, Ring-2.6-1T achieves three key breakthroughs. First, it significantly boosts agent execution capabilities, reaching open-source state-of-the-art results on benchmarks like PinchBench and ClawEval that evaluate agent adaptability, while greatly improving task decomposition and feedback correction.
Second, it introduces an innovative adjustable mechanism called "Reasoning Effort," with two intensity levels: high and xhigh. This lets developers balance cost and performance based on task complexity. The high level delivers strong results on Tau2-Bench telecom business tests, while the xhigh level reaches the capability ceiling on high-difficulty reasoning tasks such as AIME26 and GPQA Diamond.
Finally, the model adopts an asynchronous reinforcement learning architecture combined with the "Ice Stick Algorithm," effectively resolving stability issues during long training cycles for trillion-parameter models and significantly improving resource utilization.
Ring-2.6-1T is now available on
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