No, most of a modern LLM's training time is spent in RLVR, which does not "acquire information from an existing source". You can RL behaviors into a randomly initialized neural network.

This is true, but you're not going to get anywhere. The pretraining phase is necessary to immensely reduce variance in the RLVR stage. Once there, RLVR has a surprising tendency to only restrict the generated space further. This is not true of RLHF, by the way, which I find to be particularly fascinating, but I digress.