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    Home » Inside Ring-1T: Ant engineers remedy reinforcement studying bottlenecks at trillion scale
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    Inside Ring-1T: Ant engineers remedy reinforcement studying bottlenecks at trillion scale

    Emily TurnerBy Emily TurnerOctober 24, 2025No Comments4 Mins Read
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    Inside Ring-1T: Ant engineers remedy reinforcement studying bottlenecks at trillion scale
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    Inside Ring-1T: Ant engineers remedy reinforcement studying bottlenecks at trillion scale

    China’s Ant Group, an affiliate of Alibaba, detailed technical data round its new mannequin, Ring-1T, which the corporate stated is “the primary open-source reasoning mannequin with one trillion complete parameters.”

    Ring-1T goals to compete with different reasoning fashions like GPT-5 and the o-series from OpenAI, in addition to Google’s Gemini 2.5. With the brand new launch of the most recent mannequin, Ant extends the geopolitical debate over who will dominate the AI race: China or the US. 

    Ant Group stated Ring-1T is optimized for mathematical and logical issues, code technology and scientific problem-solving. 

    “With roughly 50 billion activated parameters per token, Ring-1T achieves state-of-the-art efficiency throughout a number of difficult benchmarks — regardless of relying solely on pure language reasoning capabilities,” Ant stated in a paper.

    Ring-1T, which was first launched on preview in September, adopts the identical structure as Ling 2.0 and skilled on the Ling-1T-base mannequin the corporate launched earlier this month. Ant stated this enables the mannequin to help as much as 128,000 tokens.

    To coach a mannequin as massive as Ring-1T, researchers needed to develop new strategies to scale reinforcement studying (RL).

    New strategies of coaching

    Ant Group developed three “interconnected improvements” to help the RL and coaching of Ring-1T, a problem given the mannequin's measurement and the usually massive compute necessities it entails. These three are IcePop, C3PO++ and ASystem.

    IcePop removes noisy gradient updates to stabilize coaching with out slowing inference. It helps eradicate catastrophic training-inference misalignment in RL. The researchers famous that when coaching fashions, significantly these utilizing a mixture-of-experts (MoE) structure like Ring-1T, there can typically be a discrepancy in chance calculations. 

    “This drawback is especially pronounced within the coaching of MoE fashions with RL because of the inherent utilization of the dynamic routing mechanism. Moreover, in lengthy CoT settings, these discrepancies can step by step accumulate throughout iterations and turn out to be additional amplified,” the researchers stated. 

    IcePop “suppresses unstable coaching updates by way of double-sided masking calibration.”

    The following new technique the researchers needed to develop is C3PO++, an improved model of the C3PO system that Ant beforehand established. The strategy manages how Ring-1T and different extra-large parameter fashions generate and course of coaching examples, or what they name rollouts, so GPUs don’t sit idle. 

    The best way it really works would break work in rollouts into items to course of in parallel. One group is the inference pool, which generates new information, and the opposite is the coaching pool, which collects outcomes to replace the mannequin. C3PO++ creates a token price range to manage how a lot information is processed, making certain GPUs are used effectively.

    The final new technique, ASystem, adopts a SingleController+SPMD (Single Program, A number of Information) structure to allow asynchronous operations.  

    Benchmark outcomes

    Ant pointed Ring-1T to benchmarks measuring efficiency in arithmetic, coding, logical reasoning and normal duties. They examined it towards fashions comparable to DeepSeek-V3.1-Terminus-Pondering, Qwen-35B-A22B-Pondering-2507, Gemini 2.5 Professional and GPT-5 Pondering. 

    In benchmark testing, Ring-1T carried out strongly, coming in second to OpenAI’s GPT-5 throughout most benchmarks. Ant stated that Ring-1T confirmed the most effective efficiency amongst all of the open-weight fashions it examined. 

    The mannequin posted a 93.4% rating on the AIME 25 leaderboard, second solely to GPT-5. In coding, Ring-1T outperformed each DeepSeek and Qwen.

    “It signifies that our fastidiously synthesized dataset shapes Ring-1T’s strong efficiency on programming functions, which kinds a robust basis for future endeavors on agentic functions,” the corporate stated. 

    Ring-1T exhibits how a lot Chinese language firms are investing in fashions 

    Ring-1T is simply the most recent mannequin from China aiming to dethrone GPT-5 and Gemini. 

    Chinese language firms have been releasing spectacular fashions at a fast tempo for the reason that shock launch of DeepSeek in January. Ant's guardian firm, Alibaba, lately launched Qwen3-Omni, a multimodal mannequin that natively unifies textual content, picture, audio and video. DeepSeek has additionally continued to enhance its fashions and earlier this month, launched DeepSeek-OCR. This new mannequin reimagines how fashions course of data. 

    With Ring-1T and Ant’s growth of latest strategies to coach and scale extra-large fashions, the battle for AI dominance between the US and China continues to warmth up.   

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    Emily Turner
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