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The first. Training data on a problem trumps most of the other considerations.

On the other hand, the more mainstream a programming language, the higher proportion of the training data is going to be terrible code.

I think there's an optimal ratio somewhere

I haven't seen that matter. LLM code has tended to be kinda samey regardless of language. Or at least it used to be when I spent time looking at it.

These days I moved up the ladder of abstraction, so I don't really look; the main criteria I have is how the LLM gets things done.

If it's samey regardless of language, isn't that in contradiction to your original theory? " .. the most important feature for agent performance is the popularity of the language .. "

No, not really. It's a similar flavor of output, but there's less iterations to get a correct result. The training is mostly about reducing error rates on generation.

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