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Max Ryabinin
Large-scale deep learning & research @togethercompute
Learning@home/Hivemind author (DMoE, DeDLOC, SWARM, Petals)
PhD in decentralized DL '2023
From my experience, getting a paper on decentralized DL accepted to top-level conferences can be quite tough. The motivation is not familiar to many reviewers, and standard experiment settings don't account for the problems you aim to solve.
Hence, I'm very excited to see companies like @PluralisHQ and @PrimeIntellect investing the effort to share their results and get them published at major conferences! IMO even preparing the submission forces you to be more rigorous about your experiments + outside feedback from reviewers helps you sharpen the paper's message.

Alexander Long14.7. klo 08.24
For people not familiar with AI publishing; there are 3 main conferences every year. ICML, ICLR and NeurIPS. These are technical conferences and the equivalent of journals in other disciplines - they are the main publishing venue for AI. The competition to have papers at these conferences is now at a ridiculous level, getting papers accepted is very hard, and there is a lot of concern about the review process which is quite noisey at this point. A strong paper with no flaws has around a 50% chance of being accepted, and typically a paper is submitted with reviewer changes several times until it is accepted. Despite all that, papers in these venues remain the primary stamp of legitimacy in AI world, and are probably still the primary career metrics for ML researchers (although this is weakening imo as so much of the research in the frontier labs is unpublished).
Main Track papers are significantly different to workshop papers. The main track has intense, serious peer review. Workshop papers are for preliminary work, that give some indication of an interesting result, but are either not complete or the result is not significant enough for main track. They are only required to be reviewed by the workshop reviewer pool and they don’t appear in proceedings.
Many great papers have first shown up in workshops (e.g. grokking) - but workshop and main track papers are fundamentally different things, with a fundamentally different level of impact. The only two companies in decentralised AI that have main track papers this year are @PrimeIntellect and Pluralis.
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Max Ryabinin kirjasi uudelleen
@gowthami_s @JangLawrenceK @IAmTimNguyen @ishapuri101 Distributed Training in Machine Learning🌍
Join us on July 12th as @Ar_Douillard explores key methods like FSDP, Pipeline & Expert Parallelism, plus emerging approaches like DiLoCo and SWARM—pushing the limits of global, distributed training.
Learn more:

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Thanks a lot to Ferdinand for hosting this conversation! It was a great opportunity to overview all parts of SWARM and discuss the motivation behind them in depth.
I hope this video will make decentralized DL more accessible: many ideas in the field are simpler than they seem!

Ferdinand Mom12.6.2025
The research paper video review on "Swarm Parallelism" along with the author @m_ryabinin, Distinguished Research Scientist @togethercompute is now out ! Link below 👇
For context, most decentralized training today follows DDP-style approaches requiring full model replication on each node. While practical for those with H100 clusters at their disposal, this remains out of reach for the vast majority of potential contributors, this is where SWARM comes in handy !

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