I wonder if this is an artifact of RL, where the training heavily emphasizes codegen. It may be that the model is just better at generating code than reusing li

I wonder if this is an artifact of RL, where the training heavily emphasizes codegen. It may be that the model is just better at generating code than reusing libraries, so it prefers the lowest cost approach. I also wonder if this manifests much less in contexts where the libraries/frameworks are a large part of the training set. It may be that the model doesn’t generalize well so it’s always better to use knowledge in its training set vs attempting to understand how to use a new, potentially never before seen (from the model perspective) api