Ring-Zero: Scaling Zero RL to a Trillion Parameters for Emergent Reasoning
作者: 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
分类: cs.CL
发布日期: 2026-07-14
💡 一句话要点
提出Ring-Zero以解决大规模零强化学习的推理能力问题
🎯 匹配领域: 支柱二：RL算法与架构 (RL & Architecture) 支柱九：具身大模型 (Embodied Foundation Models)
关键词: 零强化学习 大规模模型 推理能力 样本效率 认知行为 算法优化 训练管道
📋 核心要点
- 现有的零强化学习方法在大规模模型训练中面临可读性差、冗余令牌和推理深度不足等挑战。
- 本文提出了一种结合多种优化技术的训练管道，以提升大规模模型的推理质量和效率。
- 实验结果显示，Ring-2.5-1T-Zero在七个数学基准测试中表现出色，且在推理结构和简洁性方面具有明显优势。
📝 摘要（中文）
零强化学习（Zero RL）是一种无需人工标注数据的强化学习方法，已成为引导链式推理的强大范式。然而，现有研究因计算限制主要集中于小模型，未能探索大规模下的训练动态和涌现能力。为此，本文提出了一种稳定高效的训练管道，结合了算法和系统优化，如剪切重要性采样、训练-推理比率校正和混合精度控制。实验结果表明，模型在达到1万亿参数时显著提升了样本效率和性能上限，并自发发展出高级认知行为，如人性化、结构化格式、自我验证等，减少了手工设计启发式的必要性。
🔬 方法详解
问题定义：本文旨在解决现有零强化学习方法在大规模模型训练中面临的可读性差、冗余令牌和推理深度不足等问题。
核心思路：通过引入稳定高效的训练管道，结合剪切重要性采样等技术，提升模型的推理能力和样本效率。
技术框架：整体架构包括初始发现阶段和后续的精细化阶段，采用混合精度控制和训练-推理比率校正等技术模块。
关键创新：最重要的创新点在于通过大规模参数扩展（至1万亿参数）显著提升了模型的样本效率和认知能力，形成了自发的高级推理行为。
关键设计：在训练过程中，采用了剪切重要性采样、训练-推理比率校正等技术，确保模型在推理时的结构化和简洁性。损失函数和网络结构设计也经过优化，以适应大规模训练的需求。
🖼️ 关键图片
📊 实验亮点
实验结果表明，Ring-2.5-1T-Zero在七个数学基准测试中表现优异，显著提升了样本效率和性能上限。具体而言，模型在推理结构和简洁性方面的表现优于现有基线，展示了在1万亿参数规模下的涌现能力。
🎯 应用场景
该研究的潜在应用领域包括智能助手、自动化推理系统和教育技术等。通过提升模型的推理能力，Ring-Zero可以在复杂任务中提供更高质量的决策支持，推动人工智能在实际应用中的发展。
📄 摘要（原文）
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 largely restricted to small models, leaving the training dynamics and emergent capabilities at a large scale unexplored. To meaningfully explore this frontier, we aim to elicit high-quality reasoning behaviors from the model. However, we find that naive scaling often suffers from poor readability, token redundancy, and a lack of adaptive reasoning depth. To address these challenges, we present a stable and efficient training pipeline, incorporating algorithmic and system optimizations such as clipped importance sampling, training-inference ratio correction, and mixed-precision control. Our experiments offer three key findings that validate the "bitter lesson" of scaling: (1) scaling to 1T parameters significantly enhances sample efficiency and performance ceilings; (2) the training process progresses sequentially through an initial discovery phase followed by a sharpening phase; and (3) the model spontaneously develops advanced cognitive behaviors, including anthropomorphism, structured formatting, self-verification, parallel reasoning, and context anxiety, rendering hand-crafted heuristics redundant. Evaluated on seven mathematical benchmarks, Ring-2.5-1T-Zero achieves competitive performance. Additionally, to assess CoT quality beyond final-answer correctness, we propose a structured evaluation framework across three dimensions: comprehensibility, reproducibility, and efficiency, where our model demonstrates clear advantages in producing structured and concise reasoning traces. By sharing our observed emergent phenomena, we hope to provide the community with deeper insights into scaling behaviors, particularly at the 1-trillion scale.