Sparse batched finetuning of #LLM beats SOTA model editing methods to acquire a small number of pieces of knowledge and generalize; this closes the debate of whether SFT can learn knowledge. Yet, full FT fails to acquire few such knowledge triplets, while sparse FT suceeds: there should be some continuum to explore there between full-FT on large datasets and sparse-FT on few facts only.