First a few remarks that can help the analysis in general:

• use same scales for both methods
• use logarithmic y axis for the loss, to have a better idea of the trend

From the two curves I would say that you should consider:

• the gap between training and validation: you "simple transform" has a big gap, which suggests overfitting (or very different training and validation datasets)
• the loss curve: the "simple transform" does not seem to improve after 25000 steps, while the "augmentation" case could still improve (the loss isn't flat at the end)
• the trend in the loss curve: in a (x, log(y)) graph, it is easy to interpret the convergence rate (exponential, quadratic, ...). Here your augmentation seems to have a different convergence rate (not just a different factor), so it might take more epochs just to reach the same loss level.
• of course the value of the accuracy, even though it does not really say why a model performs better, of what could be improved