Ring-Zero: Scaling Zero RL to a Trillion Parameters for Emergent Reasoning

arXiv cs.CL · 18 hours ago

Researchers scaled zero reinforcement learning to a 1 trillion parameter model to study emergent reasoning capabilities without human-annotated data. The training pipeline incorporated algorithmic optimizations and demonstrated that scaling to 1 trillion parameters enhanced sample efficiency and performance while the model spontaneously developed advanced cognitive behaviors like self-verification and parallel reasoning. The resulting Ring-2.5-1T-Zero model achieved competitive performance on mathematical benchmarks and produced more structured and concise reasoning traces compared to existing approaches.