What degree of difference does validation and training loss need to have to be called good fit?

I am conducting a multi-variate time series forecasting using an LSTM model. The model architecture and other details are given below: Dataset split: (80/10/10 split) Training Data Points: 367640 Validation Data Points: 45388 Test Data Points: 40849 Features Scaling: (After data splitting) MinMax Scaler(-1,1) Model Architecture: Model: "sequential" _________________________________________________________________ Layer (type) Output Shape Param # ================================================================= lstm (LSTM) (None, 24, 256) 272384 lstm_1 (LSTM) (None, 100) 142800 dense (Dense) (None, 1) 101 ================================================================= Total params: 415,285 Trainable params: 415,285 Non-trainable params: 0 _________________________________________________________________ Activation Function in hidden layers: tanh Activation Function in output layer: None Model & Training Parameters: Learning rate: 0.0001 Batch Size: 128 Epochs: 25 ****Loss Curve **** As can be observed from the training and validation loss curve, the validation loss becomes less than the training loss after 9th epoch. From what I learned from online resources: The model fit where the validation loss is less than the training loss represnts an unkown fit. One intuitive reasoning in this case with absence of any kind of regularization is validation set examples are relatively easier to learn. What I have tried is changing batch size and learning rate such that increasing lear…