https://arxiv.org/abs/2405.20233

Skip to main content
Cornell University
We gratefully acknowledge support from the Simons Foundation, member
institutions, and all contributors. Donate
 
arxiv logo > cs > arXiv:2405.20233
[                    ]

Help | Advanced Search

[All fields        ]
Search
arXiv logo
Cornell University Logo
[                    ] GO
quick links

  * Login
  * Help Pages
  * About

Computer Science > Machine Learning

arXiv:2405.20233 (cs)
[Submitted on 30 May 2024]

Title:Grokfast: Accelerated Grokking by Amplifying Slow Gradients

Authors:Jaerin Lee, Bong Gyun Kang, Kihoon Kim, Kyoung Mu Lee
View a PDF of the paper titled Grokfast: Accelerated Grokking by
Amplifying Slow Gradients, by Jaerin Lee and 3 other authors
View PDF HTML (experimental)

    Abstract:One puzzling artifact in machine learning dubbed
    grokking is where delayed generalization is achieved tenfolds of
    iterations after near perfect overfitting to the training data.
    Focusing on the long delay itself on behalf of machine learning
    practitioners, our goal is to accelerate generalization of a
    model under grokking phenomenon. By regarding a series of
    gradients of a parameter over training iterations as a random
    signal over time, we can spectrally decompose the parameter
    trajectories under gradient descent into two components: the
    fast-varying, overfitting-yielding component and the
    slow-varying, generalization-inducing component. This analysis
    allows us to accelerate the grokking phenomenon more than $\times
    50$ with only a few lines of code that amplifies the slow-varying
    components of gradients. The experiments show that our algorithm
    applies to diverse tasks involving images, languages, and graphs,
    enabling practical availability of this peculiar artifact of
    sudden generalization. Our code is available at \url{this https
    URL}.

Comments: 15 pages, 12 figures. Project page: this https URL
Subjects: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
Cite as:  arXiv:2405.20233 [cs.LG]
          (or arXiv:2405.20233v1 [cs.LG] for this version)
          https://doi.org/10.48550/arXiv.2405.20233
          Focus to learn more
          arXiv-issued DOI via DataCite

Submission history

From: Jaerin Lee [view email]
[v1] Thu, 30 May 2024 16:35:30 UTC (7,934 KB)
Full-text links:

Access Paper:

    View a PDF of the paper titled Grokfast: Accelerated Grokking by
    Amplifying Slow Gradients, by Jaerin Lee and 3 other authors
  * View PDF
  * HTML (experimental)
  * TeX Source
  * Other Formats

view license
Current browse context:
cs.LG
< prev   |   next >
new | recent | 2405
Change to browse by:
cs
cs.AI

References & Citations

  * NASA ADS
  * Google Scholar
  * Semantic Scholar

a export BibTeX citation Loading...

BibTeX formatted citation

x
[loading...          ]
Data provided by:

Bookmark

BibSonomy logo Reddit logo
(*) Bibliographic Tools

Bibliographic and Citation Tools

[ ] Bibliographic Explorer Toggle
Bibliographic Explorer (What is the Explorer?)
[ ] Litmaps Toggle
Litmaps (What is Litmaps?)
[ ] scite.ai Toggle
scite Smart Citations (What are Smart Citations?)
( ) Code, Data, Media

Code, Data and Media Associated with this Article

[ ] Links to Code Toggle
CatalyzeX Code Finder for Papers (What is CatalyzeX?)
[ ] DagsHub Toggle
DagsHub (What is DagsHub?)
[ ] GotitPub Toggle
Gotit.pub (What is GotitPub?)
[ ] Links to Code Toggle
Papers with Code (What is Papers with Code?)
[ ] ScienceCast Toggle
ScienceCast (What is ScienceCast?)
( ) Demos

Demos

[ ] Replicate Toggle
Replicate (What is Replicate?)
[ ] Spaces Toggle
Hugging Face Spaces (What is Spaces?)
[ ] Spaces Toggle
TXYZ.AI (What is TXYZ.AI?)
( ) Related Papers

Recommenders and Search Tools

[ ] Link to Influence Flower
Influence Flower (What are Influence Flowers?)
[ ] Connected Papers Toggle
Connected Papers (What is Connected Papers?)
[ ] Core recommender toggle
CORE Recommender (What is CORE?)
[ ] IArxiv recommender toggle
IArxiv Recommender (What is IArxiv?)

  * Author
  * Venue
  * Institution
  * Topic

( ) About arXivLabs

arXivLabs: experimental projects with community collaborators

arXivLabs is a framework that allows collaborators to develop and
share new arXiv features directly on our website.

Both individuals and organizations that work with arXivLabs have
embraced and accepted our values of openness, community, excellence,
and user data privacy. arXiv is committed to these values and only
works with partners that adhere to them.

Have an idea for a project that will add value for arXiv's community?
Learn more about arXivLabs.

Which authors of this paper are endorsers? | Disable MathJax (What is
MathJax?)

  * About
  * Help

  * Click here to contact arXiv Contact
  * Click here to subscribe Subscribe

  * Copyright
  * Privacy Policy

  * Web Accessibility Assistance
  * arXiv Operational Status
    Get status notifications via email or slack