Hi @<1523701323046850560:profile|OutrageousSheep60> , thanks for your message as well. So far I have actually been using these exact functions until I noticed the following: when I run a task with these calls, everything works as expected. However, if I do a hyperparameter tuning and change some of the hyperparameters so that the additional information that is not a hyperparameter also changes, they are not adjusted. For better understanding again my concrete example: I have 3 parameters/infos 'number of layers', 'number of trainable model parameters' and 'number of neurons per layer'. The first two are hyperparameters and the last one is rather an additional information and not a hyperparameter, because it is calculated from the other two hyperparameters. If I now track all these three parameters with Task.connect(...) and vary the first two hyperparameters in a hyperparameter tuning, the last parameter remains unchanged, even if it is recalculated and re-conected in the code. Is this the expected behavior or am I doing something wrong?