In sequence modeling, interpolation and extrapolation represent two fundamentally different challenges with a significant gap in difficulty. Interpolation involves estimating values that fall within the temporal range of data the model has already observed, while extrapolation requires predicting future, unseen values beyond the observed range. Because extrapolation demands the model generalize beyond its training distribution, it is substantially harder than interpolation. This asymmetry has a critical practical implication: when working with sequential data, one must always respect the temporal order of observations during training—that is, a model should never be trained on future data to predict the past.