论文标题
物理推理的前进预测
Forward Prediction for Physical Reasoning
论文作者
论文摘要
物理推理需要前进的预测：预测下一个初始世界状态将会发生的事情。我们研究了最新的前瞻性预测模型在Phyre基准的复杂物理策略任务中的性能。我们这样做是通过将基于对象或基于像素的代表的模型合并到简单的物理策略中。我们发现，前瞻性模型可以改善物理性的性能，尤其是在涉及许多对象的复杂任务上。但是，我们还发现，这些改进取决于测试任务是火车任务的小变化，并且对全新任务模板的概括是具有挑战性的。令人惊讶的是，我们观察到，具有更好像素精度的前向预测变量并不一定会带来更好的物理性能。不过，我们最好的型号在Phyre基准上设定了新的最新技术。
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.
