Ring-Zero: Scaling Zero RL to a Trillion Parameters for Emergent Reasoning
Why CEREBRO kept it
Emergent reasoning at trillion-param scale; frontier
The text below is an automated extraction of the article at https://arxiv.org/abs/2607.12395, stored verbatim in the public cerebro-vault repository. Copyright remains with the original publisher (arxiv.org).
Computer Science > Computation and Language [Submitted on 14 Jul 2026 (v1), last revised 16 Jul 2026 (this version, v2)] Title:Ring-Zero: Scaling Zero RL to a Trillion Parameters for Emergent Reasoning View PDF HTML (experimental)Abstract:Reinforcement learning with verifiable rewards without human-annotated data, often referred to as zero RL, has emerged as a powerful paradigm for eliciting chain-of-thought reasoning. However, due to computational constraints, existing studies are largely restricted to small models, leaving the training dynamics and emergent capabilities at a large scal
Community take
Scaling to 1T parameters for barely-above-human performance is wasteful compared to human brain efficiency (billions of neurons, lightbulb power).
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