Ring-Zero: Scaling Zero RL to a Trillion Parameters for Emergent Reasoning
The paper argues that zero-RL reasoning changes qualitatively at 1 trillion parameters, not just quantitatively.
The authors say naive scaling produced messy chains of thought, including redundancy and poor readability. Their Ring-Zero pipeline adds training and system controls meant to stabilize that process. In their experiments, the 1T model moved through a discovery phase and then a sharpening phase, while showing behaviors such as self-verification, structured formatting, and parallel reasoning without hand-coded heuristics. It was tested on seven math benchmarks, with a separate framework proposed to judge chain-of-thought quality beyond final-answer accuracy. HF Daily Papers' note
The authors say naive scaling produced messy chains of thought, including redundancy and poor readability. Their Ring-Zero pipeline adds training and system controls meant to stabilize that process. In their experiments, the 1T model moved through a discovery phase and then a sharpening phase, while showing behaviors such as self-verification, structured formatting, and parallel reasoning without hand-coded heuristics. It was tested on seven math benchmarks, with a separate framework proposed to judge chain-of-thought quality beyond final-answer accuracy. HF Daily Papers' note
score 6