Test loss is the value of a loss function measured on a test set after or during model evaluation. Because it is computed on data not used directly for training, it provides a stronger signal of generalization than training loss alone.
A metric representing a model's loss against the test set. When building a model, you typically try to minimize test loss. That's because a low test loss is a stronger quality signal than a low training loss or low validation loss. A large gap between test loss and training loss or validation loss sometimes suggests that you need to increase the regularization rate.