Cool idea I guess, but if we train coding models only based on whether the code compiles or runs, won't we get models which have a pretty poor understanding of

Cool idea I guess, but if we train coding models only based on whether the code compiles or runs, won't we get models which have a pretty poor understanding of how to create good abstractions? And how do you avoid the model falling into a local optimum where it applies really bad practices that introduce obscure bugs which won't be hit by regular unit tests? Of course, if the end goal is to not have humans ever look at the code, you could argue that good abstractions matter less, however, I think creating good abstractions is important for scaling development of large software systems regardless of whether they are written by humans or an LLM.