  Physical reasoning requires forward prediction: the ability to forecast what
will happen next given some initial world state. We study the performance of
state-of-the-art forward-prediction models in the complex physical-reasoning
tasks of the PHYRE benchmark. We do so by incorporating models that operate on
object or pixel-based representations of the world into simple
physical-reasoning agents. We find that forward-prediction models can improve
physical-reasoning performance, particularly on complex tasks that involve many
objects. However, we also find that these improvements are contingent on the
test tasks being small variations of train tasks, and that generalization to
completely new task templates is challenging. Surprisingly, we observe that
forward predictors with better pixel accuracy do not necessarily lead to better
physical-reasoning performance.Nevertheless, our best models set a new
state-of-the-art on the PHYRE benchmark.
