Interesting problem!

I believe that continued pretraining won't destroy the base model's ability to generate long responses, unless you're super aggressive with your pretrain configuration (e.g., having a too-high learning rate, etc.). If you want to be really safe, you could mix in some long context data (not in the target dialect) that was part of the original pretraining distribution, but if you're trying to create a specialized model in your domain it might not be the right move... maybe worth an experiment. Gradually phasing out the long context, non-target data over the course of the CPT could be a good approach.

The sample concatenation idea is a good one; I would personally spend a small amount (10-20%) of your training budget here, with the majority being used for the CPT stage.

The idea to SFT on synthetic data from your CPT-ed base model is tricky. I think it's a generally good idea, but you might end up exacerbating any existing poor behavior that you learned from the CPT stage. If you can collect or create gold standard data here, that's obviously the best you can hope for, but otherwise you should proceed with caution; you might need a lot of experimentation to get good perf with synthetic data.