Trillion-parameter model learns reasoning via RL without human examples · 1 source tracked

A new arXiv paper, "Ring-Zero: Scaling Zero RL to a Trillion Parameters for Emergent Reasoning," details a 1T-parameter mixture-of-experts model trained with reinforcement learning. This model achieved high scores on math benchmarks without relying on human-written reasoning examples, instead learning from verifiable rewards and formatting rewards. The research highlights a shift in "reasoning engineering" from manual scripting to dynamic training processes, suggesting that complex reasoning behaviors can emerge from large-scale training with appropriate reward structures. AI

IMPACT Suggests a shift towards emergent reasoning capabilities in LLMs, potentially reducing the need for manual "reasoning engineering."

RANK_REASON The cluster describes a research paper detailing a new model architecture and training methodology. [lever_c_demoted from research: ic=1 ai=1.0]

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

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