Humans can reason compositionally whilst grounding language utterances to the
real world. Recent benchmarks like ReaSCAN use navigation tasks grounded in a
grid world to assess whether neural models exhibit similar capabilities. In
this work, we present a simple transformer-based model that outperforms
specialized architectures on ReaSCAN and a modified version of gSCAN. On
analyzing the task, we find that identifying the target location in the grid
world is the main challenge for the models. Furthermore, we show that a
particular split in ReaSCAN, which tests depth generalization, is unfair. On an
amended version of this split, we show that transformers can generalize to
deeper input structures. Finally, we design a simpler grounded compositional
generalization task, RefEx, to investigate how transformers reason
compositionally. We show that a single self-attention layer with a single head
generalizes to novel combinations of object attributes. Moreover, we derive a
precise mathematical construction of the transformer's computations from the
learned network. Overall, we provide valuable insights about the grounded
compositional generalization task and the behaviour of transformers on it,
which would be useful for researchers working in this area.

使用谷歌的翻译翻译：“该论文介绍了基于变压器的模型如何理解世界，并将语言表达与现实世界联系起来，这对研究者在这一领域的工作非常有帮助。通过在基于网格的导航任务上进行的基准测试，我们发现识别网格世界中的目标位置是模型面临的主要挑战，变压器可以泛化到更深的输入结构，并提供了一种更简单的基于调节的组合任务，以调查变压器的计算行为。

当可以进行变形器基础和组合：来自组合泛化基准测试的见解

When Can Transformers Ground and Compose: Insights from Compositional Generalization Benchmarks

We present a method for learning intrinsic reward functions to drive the
learning of an agent during periods of practice in which extrinsic task rewards
are not available. During practice, the environment may differ from the one
available for training and evaluation with extrinsic rewards. We refer to this
setup of alternating periods of practice and objective evaluation as
practice-match, drawing an analogy to regimes of skill acquisition common for
humans in sports and games. The agent must effectively use periods in the
practice environment so that performance improves during matches. In the
proposed method the intrinsic practice reward is learned through a
meta-gradient approach that adapts the practice reward parameters to reduce the
extrinsic match reward loss computed from matches. We illustrate the method on
a simple grid world, and evaluate it in two games in which the practice
environment differs from match: Pong with practice against a wall without an
opponent, and PacMan with practice in a maze without ghosts. The results show
gains from learning in practice in addition to match periods over learning in
matches only.

该研究提出了一种通过学习内在奖励函数来驱动代理在练习期间学习，避免缺乏外在任务奖励的影响的方法，并通过元梯度法来适应练习奖励参数，该方法在格子世界以及两个游戏中进行了评估，显示了在练习和比赛中同时学习的优势。