Easing Optimization Paths: a Circuit Perspective

Ambroise Odonnat, Wassim Bouaziz, Vivien Cabannes
1/4/2025
cs.LGeess.SPstat.ML

Official code: facebookresearch/pal (40 stars, last commit 2025-01-09).

Abstract

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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Code Implementations(1)

facebookresearch/palOfficial100%

PAL: Predictive Analysis & Laws of Large Language Models

407Shell, PythonSep 21, 20231 years agoNOASSERTION

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Cite this paper

@article{odonnat2025easing,
  title  = {Easing Optimization Paths: a Circuit Perspective},
  author = {Ambroise Odonnat and Wassim Bouaziz and Vivien Cabannes},
  year   = {2025},
  eprint = {2501.02362},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2501.02362v1}
}

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