in a small sense, recursive self improvement could be considered trivial theoretically and just has a bunch of practical hurdles.
if you somehow gave a current frontier llm to a full stack of the things that are involved in running an llm, including the graphics hardware its running on, the acquisition and filtering of training data and methodologies, and the ability to enact physical changes to make desired improvements - it would almost trivially make small and incremental improvements right away. like, there is zero doubt within a relatively short amount of time claude 5.5 would make a claude 5.6 that's the same but just needs slightly less compute, therefor enabling itself to build a slightly larger model for the same compute, or similar.
the big question with RSI is whether further scaling like this creates some fundamental capability jumps. the answer has been yes in the past for this, but even though its a bad predictor so far we could be at an end for scaling.