Earlier quoted context omitted.
The only thing that we are sure can't be highly compressed is knowledge, because you can only fit so much information in given entropy budget without losing fidelity. The minimal size limits of reasoning abilities are not clear at all. It could be that you don't need all that many parameters. In which case the door is open for small focused models to converge to parity with larger models in reasoning ability. If that…
> and only calling out to large models when they actually need the extra knowledge When would you want lossy encoding of lots of data bundled together with your reasoning? If it is true that reasoning can be done efficiently with fewer parameters it seems like you would always want it operating normal data searching and retrieval tools to access knowledge rather than risk hallucination. And re: this discussion of lar…
Re: Apple's accidental moat: How the "AI Loser" may end up winning
#381I find it difficult to understand the distinction between parametric knowledge and reasoning skills in LLMs. I still think of them as distinct but I understand there is significantly overlap. Arguably, they are the same thing in LLMs. So I would assume that if reasoning is high quality, using RAG could be logical (if much slower). However if the lack of parametric knowledge impacts reasoning, then use of larger models seems warranted. A dumb LLM wouldn't offer sufficient results even with all the RAG in the world.