Analysis of a Self-Supervised Training Strategy

A model is trained using a two-stage process. In the first stage, given an input context c, the model identifies an optimal output sequence, ŷ. In the second stage, the model's parameters are updated to maximize the probability of generating that same sequence ŷ, but this time conditioned on a slightly modified version of the original context, c'. What is the primary reason for using the modified context c' in the second stage instead of the original context c?

Consider a training process where the objective function is defined as Loss = log Pr(ŷ | c', z), with ŷ being an optimal prediction generated by the model itself. During training, the model's parameters are updated with the goal of minimizing this specific Loss value.