This seems obvious, but you're forgetting the inputs may actually have low entropy to begin with. Lossy compression is non-reversible, but usually the expectation is that we don't care about the parts we lost.

How might this cash out with recursive LLMs? Generalizing is very similar to compression: imagine recovering the Schrodinger equation from lots of noisy physical experiments. You might imagine that an LLM could output a set of somewhat general models from real data, and training it on data generated from those models generalizes further in future passes until maybe it caps out at the lowest entropy model (a theory of everything?)

It doesn't seem like it actually works that way with current models, but it isn't a foregone conclusion at the mathematical level at least.