4 citations · 5 across the 16 of their papers we have counts for
9 papers · 1 filter
LionMuon: Alternating Spectral and Sign Descent for Efficient Training
Arman Bolatov, Artem Riabinin, Nikita Kornilov +4
Pretraining a language model takes enormous compute, and the right optimizer can save a good part of it. Muon's spectral step gives a stronger direction than a sign step, but it is…
LoRA-TSD: Tangent-Space Spectral Descent for LoRA via Muon-Style Updates
Dmitrii Andriianov, Andrey Veprikov, Aleksandr Beznosikov
Low-rank adaptation (LoRA) is the standard way to fine-tune large models, yet when its two factors are trained independently, the update ignores the geometry of the low-rank weight…
Softsign: Smooth Sign in Your Optimizer For Better Parameter Heterogeneity Handling
Dmitrii Feoktistov, Timofey Belinsky, Andrey Veprikov +2
Sign-based and LMO-inspired optimizers have recently attracted substantial attention in deep learning due to their strong performance and low memory footprint. However, their fixed…
Why SGD is not Brownian Motion: A New Perspective on Stochastic Dynamics
Igor Ignashin, Anna Radovskaya, Andrew Semenov +7
Stochastic Gradient Descent (SGD) is commonly modeled as a Langevin process, assuming that minibatch noise acts as Brownian motion. However, this approximation relies on a continuo…
LionMuon: Alternating Spectral and Sign Descent for Efficient Training
Arman Bolatov, Artem Riabinin, Nikita Kornilov +4
In large-scale optimization, the cheapness and effectiveness of update steps are the most crucial factors for a successful optimizer. Sign-based optimizers like Lion or Signum prod…
Beyond SGD, Without SVD: Proximal Subspace Iteration LoRA with Diagonal Fractional K-FAC
Abdulla Jasem Almansoori, Maria Ivanova, Andrey Veprikov +3
Low-Rank Adaptation (LoRA) fine-tunes large models by learning low-rank updates on top of frozen weights, dramatically reducing trainable parameters and memory. In this work, we ad…
Zero-Order Optimization for LLM Fine-Tuning via Learnable Direction Sampling
Valery Parfenov, Grigoriy Evseev, Andrey Veprikov +3
Fine-tuning large pretrained language models (LLMs) is a cornerstone of modern NLP, yet its growing memory demands (driven by backpropagation and large optimizer States) limit depl…
Where Does Warm-Up Come From? Adaptive Scheduling for Norm-Constrained Optimizers
Artem Riabinin, Andrey Veprikov, Arman Bolatov +2
We study adaptive learning rate scheduling for norm-constrained optimizers (e.g., Muon and Lion). We introduce a generalized smoothness assumption under which local curvature decre…
Markovian Compression: Looking to the Past Helps Accelerate the Future
Andrey Veprikov, Vladimir Solodkin, Mikhail Rudakov +2
This paper deals with distributed optimization problems that use compressed communication to achieve efficient performance and mitigate communication bottleneck. We propose a famil…