--- title: [2607.12395] Ring-Zero: Scaling Zero RL to a Trillion... description: Ring-Zero is a research paper presenting a trillion-parameter model trained using zero RL (reinforcement learning with verifiable rewards, no human-annotated... canonical: https://daily.dev/posts/2607-12395-ring-zero-scaling-zero-rl-to-a-trillion-parameters-for-emergent-reasoning-uktx7pgvu twitter:card: summary_large_image twitter:site: @dailydotdev og:type: website og:site_name: daily.dev og:title: [2607.12395] Ring-Zero: Scaling Zero RL to a Trillion Parameters for Emergent Reasoning | daily.dev og:description: Ring-Zero is a research paper presenting a trillion-parameter model trained using zero RL (reinforcement learning with verifiable rewards, no human-annotated... og:url: https://daily.dev/posts/2607-12395-ring-zero-scaling-zero-rl-to-a-trillion-parameters-for-emergent-reasoning-uktx7pgvu og:image: https://api.daily.dev/og/posts/UKTx7PGVu.png og:image:alt: [2607.12395] Ring-Zero: Scaling Zero RL to a Trillion Parameters for Emergent Reasoning og:image:width: 1200 og:image:height: 630 og:locale: en --- > ## Documentation Index > Fetch the complete documentation index at: https://daily.dev/llms.txt > Use this file to discover all available pages before exploring further. # [2607.12395] Ring-Zero: Scaling Zero RL to a Trillion Parameters for Emergent Reasoning **[Hacker News](https://daily.dev/sources/hn)** · 3 min read · 0 upvotes · 0 comments ## Summary Ring-Zero is a research paper presenting a trillion-parameter model trained using zero RL (reinforcement learning with verifiable rewards, no human-annotated data) to study emergent reasoning at scale. The authors identify problems with naive scaling — poor readability, token redundancy, and shallow reasoning — and address them with a stable training pipeline using clipped importance sampling, training-inference ratio correction, and mixed-precision control. Key findings confirm the 'bitter lesson' of scaling: 1T parameters improve sample efficiency and performance ceilings, training progresses through discovery then sharpening phases, and the model spontaneously develops advanced behaviors like self-verification, parallel reasoning, and structured formatting without hand-crafted heuristics. Evaluated on seven math benchmarks, Ring-2.5-1T-Zero achieves competitive results. A new CoT quality evaluation framework across comprehensibility, reproducibility, and efficiency dimensions is also proposed. ## Full article daily.dev links to this article rather than hosting it. Read it at the original source: ## Similar posts on daily.dev - [Ring-Zero: Scaling Zero RL to a Trillion Parameters](https://daily.dev/posts/ring-zero-scaling-zero-rl-to-a-trillion-parameters-fiabnejwq) · Medium · 3 upvotes · 0 comments - [\[2602.04118\] Learning to Reason in 13 Parameters](https://daily.dev/posts/2602-04118-learning-to-reason-in-13-parameters-fgsqvuufg) · Hacker News · 1 upvotes · 0 comments --- Tags: [#llm](https://daily.dev/tags/llm), [#reinforcement-learning](https://daily.dev/tags/reinforcement-learning) [View this post on daily.dev](https://daily.dev/posts/2607-12395-ring-zero-scaling-zero-rl-to-a-trillion-parameters-for-emergent-reasoning-uktx7pgvu) ```json {"@context":"https://schema.org","@graph":[{"@type":"Organization","@id":"https://daily.dev/#organization","name":"daily.dev","url":"https://daily.dev","logo":{"@type":"ImageObject","url":"https://daily.dev/apple-touch-icon.png","width":180,"height":180},"sameAs":["https://twitter.com/dailydotdev","https://github.com/dailydotdev","https://www.linkedin.com/company/daily-dev-ltd"]},{"@type":"WebSite","@id":"https://daily.dev/#website","url":"https://daily.dev","name":"daily.dev","publisher":{"@id":"https://daily.dev/#organization"},"potentialAction":{"@type":"SearchAction","target":{"@type":"EntryPoint","urlTemplate":"https://daily.dev/search?q={search_term_string}"},"query-input":"required name=search_term_string"}}]} {"@context":"https://schema.org","@type":"TechArticle","headline":"[2607.12395] Ring-Zero: Scaling Zero RL to a Trillion Parameters for Emergent Reasoning","url":"https://daily.dev/posts/2607-12395-ring-zero-scaling-zero-rl-to-a-trillion-parameters-for-emergent-reasoning-uktx7pgvu","mainEntityOfPage":{"@type":"WebPage","@id":"https://daily.dev/posts/2607-12395-ring-zero-scaling-zero-rl-to-a-trillion-parameters-for-emergent-reasoning-uktx7pgvu"},"datePublished":"2026-07-17T00:59:31.595Z","dateModified":"2026-07-17T00:59:53.989Z","description":"Ring-Zero is a research paper presenting a trillion-parameter model trained using zero RL (reinforcement learning with verifiable rewards, no human-annotated...","image":"https://media.daily.dev/image/upload/f_auto,q_auto/v1/posts/1fed0de190485cbbb37a86fdf656d71b?_a=AQAEuop","thumbnailUrl":"https://media.daily.dev/image/upload/f_auto,q_auto/v1/posts/1fed0de190485cbbb37a86fdf656d71b?_a=AQAEuop","isAccessibleForFree":true,"articleSection":"Hacker News","inLanguage":"en","publisher":{"@type":"Organization","name":"daily.dev","url":"https://daily.dev","logo":{"@type":"ImageObject","url":"https://daily.dev/apple-touch-icon.png","width":180,"height":180}},"author":{"@type":"Organization","name":"Hacker News","logo":"https://media.daily.dev/image/upload/t_logo,f_auto/v1/logos/hn","url":"https://daily.dev/sources/hn"},"commentCount":0,"discussionUrl":"https://daily.dev/posts/2607-12395-ring-zero-scaling-zero-rl-to-a-trillion-parameters-for-emergent-reasoning-uktx7pgvu","interactionStatistic":[{"@type":"InteractionCounter","interactionType":{"@type":"LikeAction"},"userInteractionCount":0},{"@type":"InteractionCounter","interactionType":{"@type":"CommentAction"},"userInteractionCount":0}],"keywords":"llm,reinforcement-learning","timeRequired":"PT3M"} {"@context":"https://schema.org","@type":"BreadcrumbList","itemListElement":[{"@type":"ListItem","position":1,"name":"Home","item":"https://daily.dev"},{"@type":"ListItem","position":2,"name":"Hacker News","item":"https://daily.dev/sources/hn"},{"@type":"ListItem","position":3,"name":"[2607.12395] Ring-Zero: Scaling Zero RL to a Trillion Parameters for Emergent Reasoning"}]} ```