In this work, first, we construct and evaluate a straightforward strategy, "impute-then-detect", via combining state-of-the-art imputation methods with unsupervised anomaly detection methods, where the training data are composed of normal samples only.

As such, we prevent policies from merely exploiting heuristic rewards without improving the task reward.

Do machines and humans process language in similar ways?

Meanwhile, its"parallel" computation allowsforthesimultaneous explorationofdifferent reasoning paths andbenefits more robust and efficient execution of operations that are mutually independent (e.g. when counting individual colors for the query:"determine the maximum occurring color amongst all t-shirts"). We design IPRM as a lightweight and fully-differentiable neural module thatcanbeconveniently applied toboth transformer and non-transformer vision-language backbones.