Although I 100% agree that the core mechanism of GRPO is purely mechanical token-by-token probability generation, because RL only rewards exact final answers, the training forces the model to develop error-correction habits. This makes the output extremely like human thinking when solving a problem. It's like the order of the thinking tokens is what causes it to get that sweet, delicious reward, and this order seems like a reflection of the human thinking process.

I created a flame graph classification of thinking-token phrases into setup, execution, decomposition, verification, error correction, surrender, and deliberation, or classified as steps in an OODA loop, which is more of a reach. It literally has a verification step and, if it finds an error, an error-correction step.

If there is a verification sequence of tokens with an error-correction sequence of tokens during RL training, it will perform better; and if humans do these steps (did you proofread your reply to this comment? did you correct it?), they will perform better — which is why it is so easy to make the anthropomorphizing metaphor.

Nonetheless, the paper is 100% correct that these machines are not thinking like humans.

https://adamsohn.com/reasoning-grid/

https://adamsohn.com/lambda-variance/