Original Reddit post

I’ve been saying this for quite some time now and this paper that came out recently really puts it clearly https://arxiv.org/abs/2603.15381 The main thing is simple LLMs don’t actually learn after training They get trained once on massive data and after that everything we do like prompting fine tuning or RAG is just making a fixed system behave better not actually learn They don’t update themselves from real world experience They don’t build evolving understanding They don’t have autonomous continuous learning And I think that’s the core limitation The paper connects this with cognitive science and basically says real intelligence needs systems that can do autonomous continuous learning from interaction and experience not just predict the next token better Right now LLMs are extremely powerful but they are still pattern learners not truly adaptive systems Which is probably why they feel very smart sometimes and completely off in other situations Also interesting part is Yann LeCun is involved in this work He’s one of the pioneers of deep learning and now he’s working on world models and even raised over 1B for it That direction itself says a lot For me this confirms one thing Scaling LLMs will take us far but not all the way We need a real breakthrough to move towards real intelligence Curious what others think about this Are LLMs enough if we scale them more or are we hitting a wall here submitted by /u/HotelApprehensive402

Originally posted by u/HotelApprehensive402 on r/ArtificialInteligence