The purpose of this test is not to improve the model but to simply verify that the basic training loop is working properly. If it works, then it makes sense to revert the changes and start looking at other potential issues. The issue of loss going down without accuracy increasing is possible; for example if the model makes the same incorrect predictions but with lower confidence between epochs this will cause the loss to decrease without an actual increase in performance.