LLM reinforcement learning improves those tasks by having the agents "reason" until they hit a verifiable output state that passes a black or white test. So they learn language which is tuned to the task in a silo, never communicating with anyone or anything.

In contrast, most human communication and writing is intended for other humans, and we constantly hit failures here that we learn to iterate on and improve. Humans who do math do so with the constraints that their math needs to be reviewed by another human and in the context that all their writing needs to be understood by another human.

The process causes a warped and compressed version of language.