We handeld llm like a human brain we decopelled knowledge from the memory and build a memory layer that makes redoing things free and fast, so the llm can once it learned something solves it for free the next time

this sounds like ur explaining caching

Sounds like they're explaining magic. Because current models cannot learn and they cannot remember.

there is RL for LLMs which actually changes the weights but its more specialization than learning and wont counter the probabalistic nature of the thing

Training is not something you can just bolt on, and it generally requires even larger hardware than inference for a given model, and a huge amount of time. If you're aiming for "free", a OP claims, RL aint it.