Ring-Zero: Scaling Zero RL to a Trillion Parameters for Emergent Reasoning
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, incorporat...
| Authors | Xinyu Tang, Qianggang Cao, Yurou Liu et al. |
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| Primary category | cs.CL |
| Published | 14 July 2026 |
| Last updated at source | 16 July 2026 |
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