Ring-Zero: Scaling Zero RL to a Trillion Parameters for Emergent Reasoning
A new research paper titled 'Ring-Zero' details a method for scaling reinforcement learning to a trillion parameters for emergent reasoning. The paper is currently available on arXiv and includes links to various open-source tools and datasets.
Why it matters
Advancements in scaling reinforcement learning are critical for the development of more capable and reasoning-oriented artificial intelligence models.
Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Yuliang Zhan [ view email ] [v1] Tue, 14 Jul 2026 06:14:55 UTC (4,310 KB) Full-text links: Access Paper: 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 View PDF HTML (experimental) TeX Source view license Current browse context: cs.CL < prev | next > new | recent | 2026-07 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers ( What is Connected Papers? ) Litmaps Toggle Litmaps ( What is Litmaps?
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