Clarification needed: 'Simplify' Transfer Learning vs. 'DP-Gen Run' Finetuning for potential model updates

### Summary Hello, I have recently encountered a challenge regarding potential function development. I currently possess a potential model that accurately simulates the Phase A and Phase B of a specific alloy. I now intend to incorporate data for Phase C into this potential. Please note that no new elements are being introduced, but my current dataset for Phase C is quite limited. Consequently, I am looking to utilize transfer learning. During my research, I identified two possible workflows, but I am confused about their practical differences: The "Simplify" Method ((https://docs.deepmodeling.com/projects/dpgen/en/latest/simplify/simplify.html)): Upon testing, it appears this method is primarily designed for database screening or selection rather than direct model updating. The "DP-Gen Run" Method (https://github.com/deepmodeling/deepmd-kit/discussions/4580): This seems to behave more like a standard model refitting process. I have consulted technical support, but they were unable to provide a definitive explanation of the distinctions between these two paths. I would appreciate your guidance on the following: Which method is the most efficient and "convenient" path for adding a small amount of new phase data to an existing model? Which JSON configuration template should be used for the most up-to-date workflow? I would be extremely grateful for any insights or documentation you could provide to help resolve this. ### DeePMD-kit Version DPGEN ### Backend and its version all time ### Python Version, CUDA Version, GCC Version, LAMMPS Version, etc _No response_ ### Details that‘s all