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Hi @Manuel! Not currently, the optimizers are re-defined every time the fit is called. What specifically do you want to preserve across runs? You can modify the learning rate directly, with nf.models[0].learning_rate = new_value.

Hi @Cristian (Nixtla) I currently have a custom evaluation function (not only for the metric, but I use a specific subset of data with a special ground truth), so I cannot use the built-in validation feature for early stopping. The problem is that my model is TFT-based and the training is very slow (even with a GPU), so optimizing the number of epochs while performing hyperparameters tuning (without early stopping) is very expensive because I have to start the training again for each epoch/step value (basically, I need two different runs to try 200 and 210 epochs). If I had the ability to do an incremental fit, I could create a training loop with an early stopping mechanism using my custom evaluation function. The problem is that if I try to do this the state of the optimizer is not preserved and therefore the results are poor (even trying to decrease the learning rate, I still lose the adaptivity from the previous fit).