Abstract
Deep neural networks are powerful machines for visual pattern recognition,
but reasoning tasks that are easy for humans may still be difficult for neural
models. Humans possess the ability to extrapolate reasoning strategies learned
on simple problems to solve harder examples, often by thinking for longer. For
example, a person who has learned to solve small mazes can easily extend the
very same search techniques to solve much larger mazes by spending more time.
In computers, this behavior is often achieved through the use of algorithms,
which scale to arbitrarily hard problem instances at the cost of more
computation. In contrast, the sequential computing budget of feed-forward
neural networks is limited by their depth, and networks trained on simple
problems have no way of extending their reasoning to accommodate harder
problems. In this work, we show that recurrent networks trained to solve simple
problems with few recurrent steps can indeed solve much more complex problems
simply by performing additional recurrences during inference. We demonstrate
this algorithmic behavior of recurrent networks on prefix sum computation,
mazes, and chess. In all three domains, networks trained on simple problem
instances are able to extend their reasoning abilities at test time simply by
"thinking for longer."
Description
Can You Learn an Algorithm? Generalizing from Easy to Hard Problems with Recurrent Networks
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