That's not what happened. R1-Zero is a model per se, released with a different set of weights. Also it's not an intermediate step obtained making R1. In R1, a first SFT was performed before the RL training. While R1-Zero performed ONLY the RL training (on top of the raw V3).

Of course it's hard to argue that R1-Zero and AlphaZero are very similar, since in the case of AlfaZero (I'm referring to the chess model, not Go) only the rules were known to the model, and no human game was shown, while here:

1. The base model is V3, that saw a lot of thigs in pre-training.

2. The RL for the chain of thought has as target math problems that are annotated with the right result. This can be seen as somewhat similar to the chess game finishing with a positive, negative, or draw result. But still... it's text with a problem description.

However the similarity is that in the RL used for R1-Zero, the chain of thought to improve problem solving is learned starting cold, without showing the model any CoT to fine tune on it. However the model could sample from the V3 latent space itself that was full of CoT examples of humans, other LLMs, ...