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Zachary Charles
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Zachary Charles
@MatharyCharles
distributed machine learning @ google | sometimes mathematician
Seattle
zachcharles.com
Joined September 2012
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  • user avatar
    Zachary Charles
    @MatharyCharles
    Mar 14, 2025
    We just put out a key step for making distributed training work at larger and larger models: Scaling Laws for DiLoCo TL;DR: We can do LLM training across datacenters in a way that scales incredibly well to larger and larger models!
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    143K
  • user avatar
    Zachary Charles
    @MatharyCharles
    Apr 4, 2025
    Replying to @LinkofSunshine
    Some might even describe that vision as...abundant?
    4.4K
  • user avatar
    Zachary Charles
    @MatharyCharles
    Mar 24, 2024
    Why is Mochizuki so intent on maligning one of the few people not in his orbit trying to understand inter-universal Teichmuller theory?
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    19K
  • user avatar
    Zachary Charles
    @MatharyCharles
    Oct 4, 2023
    ML folks, a reminder that TMLR only has 2 criteria for acceptance: 1. The work is correct. 2. Some of TMLR's audience would be interested in the paper. That's it! No attempt to guess impact or enforce some notion of "novelty".
    user avatar
    Talia Ringer 🕊🪬
    @TaliaRinger
    Oct 4, 2023
    To be honest, I think the math community's approach of "accept correct (and nontrivial) work, and let time determine how it will be useful" works out far better than every other scientific community's attempt to police what will be useful ahead of time
    11K
  • user avatar
    Zachary Charles
    @MatharyCharles
    Jul 31, 2023
    1/ Our work is out! 🚨 Towards Federated Foundation Models: Scalable Dataset Pipelines for Group-Structured Learning We push federated learning research closer to LLM scales. Paper: arxiv.org/abs/2307.09619 Joint with @nicki_mitch & @KrishnaPillutla Thread below.
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  • user avatar
    Zachary Charles
    @MatharyCharles
    May 22, 2025
    We showed that an enhanced version of Lookahead worked great for LLM pre training. See arxiv.org/abs/2503.09799 - this super Lookahead method is actually just DiLoCo without the parallelism.
    user avatar
    Seunghyun Seo
    @SeunghyunSEO7
    May 21, 2025
    model averaging becomes more promising (i know it’s not new). is there anyone to try method like lookahead optimizer? ofc it seems to require bunch of engineering if we want to use this kind of things for billion scale models
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  • user avatar
    Zachary Charles
    @MatharyCharles
    Dec 13, 2021
    Inspired by a question at today's federated learning workshop at #NeurIPS2021, I've made a list of federated learning papers that have profoundly shaped my thinking on the subject. I'll try to tag authors I know are on twitter, apologies if I miss someone.
  • user avatar
    Zachary Charles
    @MatharyCharles
    Mar 13, 2024
    My paper has been on arxiv for less than 12 hours and there's already a YouTube explainer from someone. Incredible. youtu.be/fNUjjULAKYg?si…
    3K
  • user avatar
    Zachary Charles
    @MatharyCharles
    May 20, 2024
    Things I would like to include in a NeurIPS but will probably get worse reviews for: * Uncertainty * Honesty about limitations * Humor * A concise, representative set of related work instead of a compendium of every paper marginally related
    4.7K
  • user avatar
    Zachary Charles
    @MatharyCharles
    Aug 4, 2021
    Received my first 2 sentence review from a conference (#NeurIPS2021). I'm a bit frustrated that we have few mechanisms to discourage this. While barring future submissions maybe drastic, surely there's something we can do?
  • user avatar
    Zachary Charles
    @MatharyCharles
    Apr 2, 2023
    Trying to foster discussion in ICML reviews is demoralizing. Most reviewers are completely absent from the discussion, and I have yet to see a single message from an AC. At this point, I lack confidence that any of my reviews have any impact on the outcome of a paper.
    21K
  • user avatar
    Zachary Charles
    @MatharyCharles
    Aug 3, 2023
    More generally, reviewers often take a maximalist view that I think is toxic: Every paper needs to do everything. More experiments. More theory. More datasets. More algorithms. It's a conference with a page limit. Let's not pretend otherwise.
    user avatar
    Peter Richtarik
    @peter_richtarik
    Aug 2, 2023
    A good theory paper does not need experiments. A good empirical paper does not need theory. Why do most #NeurIPS2023 reviewers not understand this?
    3.2K
  • user avatar
    Zachary Charles
    @MatharyCharles
    Aug 24, 2022
    Pssst: I just put out a new paper on communication- and memory-efficient federated learning, don't tell anyone (actually, tell everyone) arxiv.org/abs/2208.09432
  • user avatar
    Zachary Charles
    @MatharyCharles
    May 31, 2023
    Fun fact: The underlying proof technique was first developed by Anastasia to study the convergence of SGD with linearly correlated noise, which is important for differentially private optimization. Check out arxiv.org/abs/2302.01463 for more!
    user avatar
    Konstantin Mishchenko
    @konstmish
    May 31, 2023
    SGD in practice usually doesn't sample data uniformly and instead goes over the dataset in epochs, which is called Random Reshuffling. We've known for some time that RR is better than SGD for convex functions and now it's been proven for nonconvex: arxiv.org/abs/2305.19259
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