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Ring-Zero: Scaling Zero RL to a Trillion Parameters for Emergent Reasoning

Reinforcement learning with verifiable rewards without human-annotated data, often referred to as zero RL, has emerged as a powerful paradigm for eliciting chain-of-thought reasoning. However, due to computational constraints, existing stud

https://arxiv.org/abs/2607.12395v2 ↗
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Xinyu Tang
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Qianggang Cao
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Yurou Liu
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Yuliang Zhan
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Yifan Li
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Yuchen Yan
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Han Peng
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Zican Dong
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Zhenduo Zhang
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Tianshu Wang
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Xinyu Kong
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arXiv Ring-Zero: Scaling Zero RL to a Trillion Parameters for Emergent Reasoning · based on signal ▸

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