Each time PRO samples, selects, and accepts a pair of hypotheses to use for training its classifier, we immediately update the pseudo-corpus by adding to it the sufficient statistics for the higher-scoring sentence, i.e., the positive example, in the pair.
while the pseudo-corpus is initialized with the statistics from previous iterations, it soon gets dominated by statistics for the positive examples that are being accepted as training sentence pairs at the current iteration, since there are more of them than there are one-best hypotheses from previous iterations.