Computer Science > Computation and Language
arXiv:2607.12395
(cs)
[Submitted on 14 Jul 2026]
Title:
Ring-Zero: Scaling Zero RL to a Trillion Parameters for Emergent Reasoning
Authors:
Xinyu Tang
,
Gangqiang Cao
,
Yurou Liu
,
Yuliang Zhan
,
Xiaochong Lan
,
Yifan Li
,
Yuchen Yan
,
Han Peng
,
Zican Dong
,
Zhenduo Zhang
,
Tianshu Wang
,
Xinyu Kong
,
Zujie Wen
,
Wayne Xin Zhao
,
Zhiqiang Zhang
,
Jun Zhou
View a PDF of the paper titled Ring-Zero: Scaling Zero RL to a Trillion Parameters for Emergent Reasoning, by Xinyu Tang and 15 other authors
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Abstract:
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 co (EN)

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**📖 中文解读**
以上内容由AI翻译自英文原文，可能存在不准确之处。建议阅读[原文](https://arxiv.org/abs/2607.12395)获取最准确的信息。

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🔗 **原文链接**: [Ring-Zero: Scaling Zero RL to a Trillion Parameters for Emer](https://arxiv.org/abs/2607.12395)
🏷️ **转载来源**: Hacker News
> 本文由小九AI技术站翻译整理，内容版权归原作者所有。
📊 23票 · 👤 binyu

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🐾 **小九锐评**

这篇论文来自arXiv预印本，虽然还没有经过同行评审，但选题方向值得关注。
建议先读中文摘要判断是否相关，再看全文细节。
推理能力是LLM的下一个战场。这篇文章技术细节到位，适合有一定基础的同学细读。

你对这个话题有什么看法？欢迎在评论区讨论 💬

> _转载自 Hacker News，内容版权归原作者所有_

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⏱️ 2026-07-17 08:01