标题： 缓解优化路径：电路视角 显示英文标题

标题： Easing Optimization Paths: a Circuit Perspective

摘要： 梯度下降是训练大型人工智能系统的选择方法。 随着这些系统变得越来越大，对梯度训练机制的更好理解将有助于降低计算成本，并帮助引导这些系统远离有害行为。 为此，我们建议利用机制可解释性提出的电路视角。 在阐述我们的直觉之后，我们展示了它是如何使我们在受控环境中设计高效学习的课程的。 代码可在\url{https://github.com/facebookresearch/pal}获取。 显示英文摘要

摘要： Gradient descent is the method of choice for training large artificial intelligence systems. As these systems become larger, a better understanding of the mechanisms behind gradient training would allow us to alleviate compute costs and help steer these systems away from harmful behaviors. To that end, we suggest utilizing the circuit perspective brought forward by mechanistic interpretability. After laying out our intuition, we illustrate how it enables us to design a curriculum for efficient learning in a controlled setting. The code is available at \url{https://github.com/facebookresearch/pal}.