This is about to change very soon. Unlike many other domains (such as greenfield scientific discovery), most coding problems for which we can write tests and benchmarks are "verifiable domains".

This means an LLM can autogenerated millions of code problem prompts, attempt millions of solutions (both working and non-working), and from the working solutions, penalize answers that have poor performance. The resulting synthetic dataset can then be used as a finetuning dataset.

There are now reinforcement finetuning techniques that have not been incorporated into the existing slate of LLMs that will enable finetuning them for both plausibility AND performance with a lot of gray area (like readability, conciseness, etc) in between.

What we are observing now is just the tip of a very large iceberg.