I have published an addendum to an article I wrote about AlphaChip (https://vighneshiyer.com/misc/ml-for-placement/) at the very bottom that addresses this rebuttal from Google and the AlphaChip algorithm in general.
In short, I think the Nature authors have made some reasonable criticisms regarding the training methodology employed by the ISPD authors, but the extreme compute cost and runtime of AlphaChip still makes it non-competitive with commercial autofloorplanners and AutoDMP. Regardless, I think the ISPD authors owe the Nature authors an even more rigorous study that addresses all their criticisms. Even if they just try to evaluate the pre-trained checkpoint that Google published, that would be a useful piece of data to add to the debate.
In short, I think the Nature authors have made some reasonable criticisms regarding the training methodology employed by the ISPD authors, but the extreme compute cost and runtime of AlphaChip still makes it non-competitive with commercial autofloorplanners and AutoDMP. Regardless, I think the ISPD authors owe the Nature authors an even more rigorous study that addresses all their criticisms. Even if they just try to evaluate the pre-trained checkpoint that Google published, that would be a useful piece of data to add to the debate.