RL adds a lot of capability in the areas where it can be applied, but I don't think it really changes the fundamental nature of LLMs - they are still predicting training set continuations, but now trying to predict/select continuations that amount to reasoning steps steering the output in a direction that had been rewarded during training.

At the end of the day it's still copying, not learning.

RL seems to mostly only generalize in-domain. The RL-trained model may be able to generate a working C compiler, but the "logical reasoning" it had baked into it to achieve this still doesn't stop it from telling you to walk to the car wash, leaving your car at home.

There may still be more surprises coming from LLMs - ways to wring more capability out of them, as RL did, without fundamentally changing the approach, but I think we'll eventually need to adopt the animal intelligence approach of predicting the world rather than predicting training samples to achieve human-like, human-level intelligence (AGI).