Ring-Zero: Scaling Zero RL to a Trillion Parameters for Emergent Reasoning
英文标题：Ring-Zero: Scaling Zero RL to a Trillion Parameters for Emergent Reasoning
arXiv:2607.12395v1 Announce Type: new 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 constraints, existing studies are lar
打开原文相关 1.6 · 机会 0
中文
arXiv:2607.12395v1 Announce Type: new 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 constraints, existing studies are lar
中文全文翻译正在补齐，可先查看下方英文原文。
English / 原文
arXiv:2607.12395v1 Announce Type: new 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 constraints, existing studies are lar