用电路视角优化训练路径，降低大模型计算成本。
Easing Optimization Paths: a Circuit Perspective
- 从电路视角理解梯度训练机制
- 设计高效学习课程，在控制环境中实现加速
- 适合关注模型训练效率与可解释性的研究者
梯度下降是训练大型人工智能系统的主要方法。随着系统规模扩大，深入理解梯度训练的内在机制，有助于降低计算成本，并引导系统避免有害行为。为此，我们提出采用机械可解释性中的电路视角。在阐明直觉后，我们展示该视角如何在受控环境中设计高效学习课程。代码已公开于 <https://github.com/facebookresearch/pal>。
原文摘要 · Abstract (English)
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}.
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