In December 2024, during the frenzied adoption of LLM coding assistants, we became aware that such tools tended—unsurprisingly—to produce Go code in a style similar to the mass of Go code used during training, even when there were newer, better ways to express the same idea. Less obviously, the same tools often refused to use the newer ways even when directed to do so in general terms such as “always use the latest idioms of Go 1.25.” In some cases, even when explicitly told to use a feature, the model would deny that it existed. [...] To ensure that future models are trained on the latest idioms, we need to ensure that these idioms are reflected in the training data, which is to say the global corpus of open-source Go code.
In December 2024, during the frenzied adoption of LLM coding assistants, we became aware that such tools tended—unsurprisingly—to produce Go code in a style similar to the mass of Go code used during training, even when there were newer, better ways to express the same idea. Less obviously, the same tools often refused to use the newer ways even when directed to do so in general terms such as “always use the latest idioms of Go 1.25.” In some cases, even when explicitly told to use a feature, the model would deny that it existed. [...] To ensure that future models are trained on the latest idioms, we need to ensure that these idioms are reflected in the training data, which is to say the global corpus of open-source Go code.