Cobalt testcase has only energy-based training items. About 90% of these items require energy minimization, yet the training error does not fluctuate as shown in Fig. . This shows that the surrogate error is close to the true error for this dataset. Otherwise, the error would fluctuate between iterations since the surrogate error is used for the error minimization in each iteration. For some of the random runs, SLSQP does not show any progress initially. One possible explanation is that when the initial parameters are from a non-smooth part of the optimization space that cause high gradients, the optimizer fails to escape (Fig. ). Small noise that is added when a stall in progress is detected stimulates progress as expected.