It depends on your model and your training loss might be higher than the validation loss, e.g. if you are using dropout during training. This will decrease the capacity of your training model and might thus yield a higher loss.
Also, it might be possible that your data split is not completely random (or you were unlucky, if your dataset is small) so that easier samples are in the validation set.
Of course this effect should disappear if your dataset is sufficiently large and if you split randomly.