Ant Group's Bailing InclusionAI, together with Wayne Xin Zhao's team, released the Ring-Zero paper on July 14, pushing zero-RL training to the 1T-parameter scale for the first time; the resulting Ring-2.5-1T-Zero achieves competitive performance on seven math benchmarks, and checkpoints are open-sourced to Hugging Face. Zero-RL doesn't rely on human annotation, but uses verifiable rewards to directly stimulate chain-of-thought on a cold-start model. Previously constrained by compute, this path had mostly stopped at the 7B–32B range; Ring-Zero pushes the scale to 1T through clipped importance sampling, training-inference ratio correction, mixed-precision control, and other systems engineering. The most valuable part of the paper is not "it can be done", but "what the result looks like". The authors give three points: the 1T scale significantly improves sample efficiency and the performance ceiling; training is split into a "discovery" phase and a "sharpening" phase; the model spontaneously emerges human-like expression, structured layout, self-verification, parallel reasoning, context anxiety, and other high-level cognitive behaviors, making hand-designed reward heuristics redundant. Regarding CoT quality, the authors propose a "readability, reproducibility, efficiency" three-dimensional framework, which is closer to industrial deployment than just comparing final answers. This is another large-scale validation of the "post-training is product power" narrative after DeepSeek R1; the feasibility of 1T-level zero-RL is opened up, and subsequent combinations like MoE, long context, and reasoning cost control are worth continuing to watch.