Aside from modeling details, much of the remaining progress in VLN has come from adjusting the training regime -adding auxiliary losses / rewards [17, 29] , mitigating exposure bias during training<cite> [4,</cite> 29] , or reducing data sparsity by incorporating synthetically generated data augmentation [9, 26] .