Ring-Zero Scales Zero RL to Trillion-Parameter Models for Reasoning
July 16, 2026
Scaling reinforcement learning with verifiable rewards to trillion-parameter models enables emergent reasoning capabilities without human-annotated data. The method addresses scaling challenges such as token redundancy, poor readability, and inconsistent reasoning depth found in naive implementations.
HOW THIS AFFECTS YOU
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builderThis points toward a path for training reasoning models without expensive human preference data.
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researcherYou can study training dynamics of verifiable rewards at the trillion-parameter scale.