> If an assistant offered up suggested code, the code ran successfully, and the user accepted the code, that was a positive signal, a sign that the assistant had gotten it right. If the user rejected the code, or if the code failed to run, that was a negative signal, and when the model was retrained, the assistant would be steered in a different direction.

> This is a powerful idea, and no doubt contributed to the rapid improvement of AI coding assistants for a period of time. But as inexperienced coders started turning up in greater numbers, it also started to poison the training data.

It is not just `inexperienced coders` that make this signal pretty much useless, I mostly use coding assistants for boilerplate, I will accept the suggestion then delete much of what it produced, especially in the critical path.

For many users, this is much faster then trying to get another approximation

     :,/^}/-d

It would be a mistake to think that filtering out jr devs will result in good data as the concept is flawed in general. Accepting output may not have anything to do with correctness of the provided content IMHO.