Ring-Zero: Scaling Zero RL to a Trillion Parameters for Emergent Reasoning
- Published
- Source
- arXiv
- Paper number
- 631
- Field
- LLMs / NLP
- arXiv ID
- 2607.12395
Key points
- It stabilized zero reinforcement learning for a 1-trillion-parameter model by combining importance-ratio clipping, training-inference ratio correction, and mixed-precision control.
- Scaling the model to 1 trillion parameters increased sample efficiency and the performance ceiling, with training progressing from a discovery phase to a refinement phase.
- Behaviors such as structured formatting, self-verification, parallel reasoning, and context anxiety emerged without explicit rules.
- It proposed a framework for evaluating reasoning processes through understandability, reproducibility, and efficiency as well as accuracy, providing a tool for analyzing large-scale reasoning training.
- Empirical evidence is concentrated on seven math benchmarks and a 1-trillion-parameter configuration, leaving open whether the results generalize directly to other tasks and smaller computation budgets.
Paper links
External research summaries. These are not HDATF publications or measured product results.