Positional embeddings are tricky - it very much depends on the specific embedding method chosen. Some advanced methods allow conserved or even slightly improved performance with context length increased beyond what was used for the main pretraining run.

As often is the case with these large models, you can change it with some finetuning on longer context samples from the same dataset, with what is really a small amount of compute invested compared to the million hours spent on training the thing.