The data is not the problem. I could train on any of the public datasets combined with my own data. And here comes my point: The result I'd achieve by training

The data is not the problem. I could train on any of the public datasets combined with my own data. And here comes my point: The result I'd achieve by training on that combined dataset from scratch cannot be achieved cheaper by utilizing an already pre-trained model of the huge generic dataset plus whatever additional training with the large domain-specific dataset. From what I understand: If you fine-tune only with the domain specific data and just enough so that the model picks up that knowledge it will have forgotten most of its generic knowledge already. If you train on the combined dataset it will take as many epochs for the domain knowledge to shine through as for training the original model. It would cost the same as training from scratch. You need enough tokens but you also need to train your model the right amount on them. Too much or too little training is bad and the base models we have are just trained for the sweet spot and will tolerate a little bit of fine-tuning to adapt their behaviour but not nearly enough to teach them new facts. I'm not an expert, so I could be wrong. What makes me a bit confident is that I have not yet found a single project that reports to have used the approach you suggest successfully.