  Large natural language models (such as GPT-3 or T5) demonstrate impressive
abilities across a range of general NLP tasks. Here, we show that the knowledge
embedded in such models provides a useful inductive bias, not just on
traditional NLP tasks, but also in the nontraditional task of training a
symbolic reasoning engine. We observe that these engines learn quickly and
generalize in a natural way that reflects human intuition. For example,
training such a system to model block-stacking might naturally generalize to
stacking other types of objects because of structure in the real world that has
been partially captured by the language describing it. We study several
abstract textual reasoning tasks, such as object manipulation and navigation,
and demonstrate multiple types of generalization to novel scenarios and the
symbols that comprise them. We also demonstrate the surprising utility of
\textit{compositional learning}, where a learner dedicated to mastering a
complicated task gains an advantage by training on relevant simpler tasks
instead of jumping straight to the complicated task.
