Ring-1T

Ring-1T is a large open-weight reasoning model from Bailing that sits at the top of the ring family and is designed around deep natural language inference rather than general conversation. It is built on the Ling 2.0 architecture and the Ling-1T-base foundation, giving it a clear lineage within Bailing's research direction toward verifiable, proof-oriented reasoning. The model is structured as a mixture-of-experts network with roughly one trillion total parameters while activating only about fifty billion parameters per token, an arrangement intended to keep deep reasoning affordable to run at inference time compared with dense trillion-parameter designs. A long context window, reported by the hosting provider at up to 131K tokens, makes it possible to keep entire problem statements, proofs, or multi-file code reasoning chains in memory at once. The training story for Ring-1T centers on large-scale verifiable-reward reinforcement learning, an approach where the model is rewarded for producing outputs that can be mechanically checked, rather than for surface plausibility. Bailing combined this RLVR pipeline with a self-developed icepop stabilization method to keep training stable across such a large parameter count, and ran it inside their ASystem reinforcement learning framework. The result is a model aimed squarely at intricate challenges: olympiad-level mathematics, theoretical physics derivations, and competitive programming problems of the sort seen at the IMO and ICPC World Finals, where it is reported to set a leading open-source mark. Practically, Ring-1T fits teams that need an open model they can self-host for hard reasoning workloads, from research labs exploring proof generation to engineers building assistants for advanced math, science, and code contests.

Ring-1T is a large open-weight reasoning model from Bailing that sits at the top of the ring family and is designed around deep natural language inference rather than general conversation. It is built on the Ling 2.0 architecture and the Ling-1T-base foundation, giving it a clear lineage within Bailing's research direction toward verifiable, proof-oriented reasoning. The model is structured as a mixture-of-experts network with roughly one trillion total parameters while activating only about fifty billion parameters per token, an arrangement intended to keep deep reasoning affordable to run at inference time compared with dense trillion-parameter designs. A long context window, reported by the hosting provider at up to 131K tokens, makes it possible to keep entire problem statements, proofs, or multi-file code reasoning chains in memory at once.