Forward Prediction for Physical Reasoning
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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 complex physical-reasoning tasks. 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 improve the performance of physical-reasoning agents, particularly on complex tasks that involve many objects. However, we also find that these improvements are contingent on the training tasks being similar to the test tasks, and that generalization to different tasks is more 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 for physical reasoning.
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