It means something that is too out-of-data. For example if you try to make an LLM write a program in a strange or very new language it will struggle in non-trivial tasks.
There was research on LLMs training and distillation that if two models have a similar architecture (probably the case for Xai) the "master" model will distill knowledge to the model even if its not in the distillation data. So they probably need to train a new model from scratch.
(sorry i don't remember the name but there was an example with a model liking howl to showcase this)
You should read the 6th page of the paper (and page 5 for architecture breakdown), they show that they are compressing the vision tokens with convolution to keep a strong semantic understanding and keep a small amount of tokens.
Vision tokens are a good compression medium because with one vision token you have one vector of N elements, but with textual tokens you have M vectors of N elements, because one vision token represent multiple pixels (and possibly multiple words). This is why its a good compression medium for compute.
It will never be as precise as textual tokens but it can be really good as they show in the paper.
But I agree with you for the authoritarian logics in Europe (even America) with Chat Control and other actions like the French gov. just did....