Comment by ACCount37

That's the issue I have with criticism of LLMs.

A lot of people say "LLMs are fundamentally flawed, a dead end, and can never become AGI", but on deeper examination? The arguments are weak at best, and completely bogus at worst. And then the suggested alternatives fail to outperform the baseline.

I think by now, it's clear that pure next token prediction as a training objective is insufficient in practice (might be sufficient in the limit?) - which is why we see things like RLHF, RLAIF and RLVR in post-training instead of just SFT. But that says little about the limitations of next token prediction as an architecture.

Next token prediction as a training objective still allows an LLM to learn an awful lot of useful features and representations in an unsupervised fashion, so it's not going away any time soon. But I do expect to see modified pre-training, with other objectives alongside it, to start steering the models towards features that are useful for inference early on.