Scaling Zero Reinforcement Learning to 1 Trillion Parameters for Emergent Reasoning

This paper presents Ring-Zero, a method for scaling Zero Reinforcement Learning (RL) to 1 trillion parameters, addressing challenges like readability and token redundancy through algorithmic and system optimizations. It demonstrates that scaling enhances sample efficiency, performance, and emergent reasoning capabilities such as self-verification and parallel reasoning.

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