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career metrics for ML researchers (although this is weakening imo as so much of the research in the frontier labs is unpublished).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.

¡La revisión en video del artículo de investigación sobre "Paralelismo de Enjambre" junto con el autor @m_ryabinin, Científico Investigador Distinguido @togethercompute ya está disponible! Enlace abajo 👇 Para dar contexto, la mayoría de los entrenamientos descentralizados hoy en día siguen enfoques estilo DDP que requieren una replicación completa del modelo en cada nodo. Si bien es práctico para aquellos que tienen clústeres H100 a su disposición, esto sigue estando fuera del alcance de la gran mayoría de los posibles contribuyentes, ¡aquí es donde SWARM resulta útil!