Artem Riabinin — accepted AI-conference papers

2026

From Muon to Gluon: Bridging Theory and Practice of LMO-based Optimizers for LLMs

Artem Riabinin, Egor Shulgin, Kaja Gruntkowska, Peter Richtarik

ICML 2026poster

Recent developments in deep learning optimization have brought about radically new algorithms based on the Linear Minimization Oracle (LMO) framework, such as Muon and Scion. After over a decade of Adam's dominance, these LMO-based methods are emerging as viable replacements, offering several practi…

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2025

Error Feedback under $(L_0,L_1)$-Smoothness: Normalization and Momentum

Sarit Khirirat, Abdurakhmon Sadiev, Artem Riabinin, Eduard Gorbunov, Peter Richtárik

NeurIPS 2025poster

We provide the first proof of convergence for normalized error feedback algorithms across a wide range of machine learning problems. Despite their popularity and efficiency in training deep neural networks, traditional analyses of error feedback algorithms rely on the smoothness assumption that doe…

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