Heck, OpenAI can't even get their models to reliably do what they intend, so yeah.
If you don't get the difference, I get there isn't much that you know about how instruction selection works, across all those approaches.
Do any of the optimizations you mentioned affect the underlying behavior of the run code? Does the source ever lead to a different observable behavior? If not, it doesn't fit to LLM use, where this is very much the case.
Which for example, unless you can exactly replicate the world every single time a PGO data gathering session is done, there are no guarantees that the compiler will generate exactly the same machine code pattern, when given various PGO files generated at different occurrences.
But, high level languages don't really lose precision in any way that matters (except when you need extreme performance, resistance to timing attacks, or niche stuff like that). Natural language on the other hand loses precision that's usually important, so I don't see how your comparison is valid.