Comment by codebje

There's a good number of heuristic choices in compilation where, maybe, you could get more optimal outcomes with machine learning - but at the cost of compilation resources, both time and space, and possibly determinism too.

As an example, register allocation is graph colouring, and thus NP complete; a model for producing an allocation plan is learning heuristics that might look at more features in combination than the ones hand-crafted into the compiler. An LLM for the job might do better than a more focused model like a GNN, due to sheer size, the effectiveness of transformers, or magic. But it probably won't do an overall better job than the handcrafted heuristics, because those handcrafted heuristics also tend to compile very, very fast with a small memory footprint, and can be debugged (more) easily when they go wrong.