Next we investigate to which extent the difference between OS and DCS arise from model and data specific biases. Fig. shows how all three scores develop during the course of optimization. We observe that the OS and MCS diverge in the course of training. This suggests that optimization exploits features unique to the OS to obtain improvements that do not generalize to the MCS, despite being trained on exactly the same data. It can also be observed that the larger part of the difference between OC and DCS is due to data specific biases. suggests that optimization should be stopped early once the control scores cease to increase.