Quick check, do you really need Ge-Se-Te or Ge-Sn-Te (commonly, GST)? If it's the latter, we already have such a potential provided in the package.
Otherwise, you are in for a ride, but a potentially very rewarding one!
Your methodology is sort of correct, but you don't fit separately for 1-3, you combine those steps into one. That is, you include a bunch of basic crystal structures for all combinations in one training set (which you can test against the crystal experimental data). Then, instead of your step 4, you use active learning on a set of random alloys (and/or amorphous structures) of Ge-Se-Te to refine the potential for the full system. Ideally, for different stoichiometries! This is similar to the workflow we used for TiSi2 in the tutorial below, just a bit more complex for your case since you have 3 elements.
https://docs.quantumatk.com/tutorials/mtp-training-c-am-TiSi/mtp-training-c-am-TiSi.html