  Abstraction reasoning is a long-standing challenge in artificial
intelligence. Recent studies suggest that many of the deep architectures that
have triumphed over other domains failed to work well in abstract reasoning. In
this paper, we first illustrate that one of the main challenges in such a
reasoning task is the presence of distracting features, which requires the
learning algorithm to leverage counterevidence and to reject any of the false
hypotheses in order to learn the true patterns. We later show that carefully
designed learning trajectory over different categories of training data can
effectively boost learning performance by mitigating the impacts of distracting
features. Inspired by this fact, we propose feature robust abstract reasoning
(FRAR) model, which consists of a reinforcement learning based teacher network
to determine the sequence of training and a student network for predictions.
Experimental results demonstrated strong improvements over baseline algorithms
and we are able to beat the state-of-the-art models by 18.7% in the RAVEN
dataset and 13.3% in the PGM dataset.
