Saved in:
| Main Authors: | Modoranu, Ionut-Vlad, Zmushko, Philip, Schultheis, Erik, Safaryan, Mher, Alistarh, Dan |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2602.02016 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
MatryoshkaLoRA: Learning Accurate Hierarchical Low-Rank Representations for LLM Fine-Tuning
by: Modoranu, Ionut-Vlad, et al.
Published: (2026)
by: Modoranu, Ionut-Vlad, et al.
Published: (2026)
The Iterative Optimal Brain Surgeon: Faster Sparse Recovery by Leveraging Second-Order Information
by: Wu, Diyuan, et al.
Published: (2024)
by: Wu, Diyuan, et al.
Published: (2024)
FFT-based Dynamic Subspace Selection for Low-Rank Adaptive Optimization of Large Language Models
by: Modoranu, Ionut-Vlad, et al.
Published: (2025)
by: Modoranu, Ionut-Vlad, et al.
Published: (2025)
LDAdam: Adaptive Optimization from Low-Dimensional Gradient Statistics
by: Robert, Thomas, et al.
Published: (2024)
by: Robert, Thomas, et al.
Published: (2024)
Unified Scaling Laws for Compressed Representations
by: Panferov, Andrei, et al.
Published: (2025)
by: Panferov, Andrei, et al.
Published: (2025)
MicroAdam: Accurate Adaptive Optimization with Low Space Overhead and Provable Convergence
by: Modoranu, Ionut-Vlad, et al.
Published: (2024)
by: Modoranu, Ionut-Vlad, et al.
Published: (2024)
LoRDO: Distributed Low-Rank Optimization with Infrequent Communication
by: Jovanović, Andrej, et al.
Published: (2026)
by: Jovanović, Andrej, et al.
Published: (2026)
Towards Robust Scaling Laws for Optimizers
by: Volkova, Alexandra, et al.
Published: (2026)
by: Volkova, Alexandra, et al.
Published: (2026)
Error Feedback Can Accurately Compress Preconditioners
by: Modoranu, Ionut-Vlad, et al.
Published: (2023)
by: Modoranu, Ionut-Vlad, et al.
Published: (2023)
CAGE: Curvature-Aware Gradient Estimation For Accurate Quantization-Aware Training
by: Tabesh, Soroush, et al.
Published: (2025)
by: Tabesh, Soroush, et al.
Published: (2025)
LLMQ: Efficient Lower-Precision Pretraining for Consumer GPUs
by: Schultheis, Erik, et al.
Published: (2025)
by: Schultheis, Erik, et al.
Published: (2025)
Optimizers Qualitatively Alter Solutions And We Should Leverage This
by: Pascanu, Razvan, et al.
Published: (2025)
by: Pascanu, Razvan, et al.
Published: (2025)
Quartet II: Accurate LLM Pre-Training in NVFP4 by Improved Unbiased Gradient Estimation
by: Panferov, Andrei, et al.
Published: (2026)
by: Panferov, Andrei, et al.
Published: (2026)
GradSkip: Communication-Accelerated Local Gradient Methods with Better Computational Complexity
by: Maranjyan, Artavazd, et al.
Published: (2022)
by: Maranjyan, Artavazd, et al.
Published: (2022)
On Biased Compression for Distributed Learning
by: Beznosikov, Aleksandr, et al.
Published: (2020)
by: Beznosikov, Aleksandr, et al.
Published: (2020)
Grid Games: The Power of Multiple Grids for Quantizing Large Language Models
by: Egiazarian, Vage, et al.
Published: (2026)
by: Egiazarian, Vage, et al.
Published: (2026)
FRUGAL: Memory-Efficient Optimization by Reducing State Overhead for Scalable Training
by: Zmushko, Philip, et al.
Published: (2024)
by: Zmushko, Philip, et al.
Published: (2024)
Hogwild! Inference: Parallel LLM Generation via Concurrent Attention
by: Rodionov, Gleb, et al.
Published: (2025)
by: Rodionov, Gleb, et al.
Published: (2025)
Model Compression with Exact Budget Constraints via Riemannian Manifolds
by: Helcig, Michael, et al.
Published: (2026)
by: Helcig, Michael, et al.
Published: (2026)
Hybrid Decentralized Optimization: Leveraging Both First- and Zeroth-Order Optimizers for Faster Convergence
by: Ansaripour, Matin, et al.
Published: (2022)
by: Ansaripour, Matin, et al.
Published: (2022)
MatGPTQ: Accurate and Efficient Post-Training Matryoshka Quantization
by: Kleinegger, Maximilian, et al.
Published: (2026)
by: Kleinegger, Maximilian, et al.
Published: (2026)
Convergence Rate Analysis of the AdamW-Style Shampoo: Unifying One-Sided and Two-Sided Preconditioning
by: Li, Huan, et al.
Published: (2026)
by: Li, Huan, et al.
Published: (2026)
Behemoth: Benchmarking Unlearning in LLMs Using Fully Synthetic Data
by: Iofinova, Eugenia, et al.
Published: (2026)
by: Iofinova, Eugenia, et al.
Published: (2026)
Efficient Data Selection at Scale via Influence Distillation
by: Nikdan, Mahdi, et al.
Published: (2025)
by: Nikdan, Mahdi, et al.
Published: (2025)
Purifying Shampoo: Investigating Shampoo's Heuristics by Decomposing its Preconditioner
by: Eschenhagen, Runa, et al.
Published: (2025)
by: Eschenhagen, Runa, et al.
Published: (2025)
Label Privacy in Split Learning for Large Models with Parameter-Efficient Training
by: Zmushko, Philip, et al.
Published: (2024)
by: Zmushko, Philip, et al.
Published: (2024)
Communication-Efficient Federated Learning With Data and Client Heterogeneity
by: Zakerinia, Hossein, et al.
Published: (2022)
by: Zakerinia, Hossein, et al.
Published: (2022)
RoSA: Accurate Parameter-Efficient Fine-Tuning via Robust Adaptation
by: Nikdan, Mahdi, et al.
Published: (2024)
by: Nikdan, Mahdi, et al.
Published: (2024)
Apertus LLM Family Expansion via Distillation and Quantization
by: Panferov, Andrei, et al.
Published: (2026)
by: Panferov, Andrei, et al.
Published: (2026)
4-bit Shampoo for Memory-Efficient Network Training
by: Wang, Sike, et al.
Published: (2024)
by: Wang, Sike, et al.
Published: (2024)
EvoPress: Accurate Dynamic Model Compression via Evolutionary Search
by: Sieberling, Oliver, et al.
Published: (2024)
by: Sieberling, Oliver, et al.
Published: (2024)
SGD for Variational Inference: Tackling Unbounded Variance via Preconditioning and Dynamic Batching
by: Labarrière, Hippolyte, et al.
Published: (2026)
by: Labarrière, Hippolyte, et al.
Published: (2026)
Statistically-Lossless Quantization of Large Language Models
by: Helcig, Michael, et al.
Published: (2026)
by: Helcig, Michael, et al.
Published: (2026)
Powerset Convolutional Neural Networks
by: Wendler, Chris, et al.
Published: (2019)
by: Wendler, Chris, et al.
Published: (2019)
Just a Simple Transformation is Enough for Data Protection in Vertical Federated Learning
by: Semenov, Andrei, et al.
Published: (2024)
by: Semenov, Andrei, et al.
Published: (2024)
Understanding and Improving Shampoo and SOAP via Kullback-Leibler Minimization
by: Lin, Wu, et al.
Published: (2025)
by: Lin, Wu, et al.
Published: (2025)
Solving Dense Linear Systems Faster Than via Preconditioning
by: Dereziński, Michał, et al.
Published: (2023)
by: Dereziński, Michał, et al.
Published: (2023)
Sign-SGD via Parameter-Free Optimization
by: Medyakov, Daniil, et al.
Published: (2025)
by: Medyakov, Daniil, et al.
Published: (2025)
MT-DAO: Multi-Timescale Distributed Adaptive Optimizers with Local Updates
by: Iacob, Alex, et al.
Published: (2025)
by: Iacob, Alex, et al.
Published: (2025)
Simple Opinion Dynamics for No-Regret Learning
by: Lazarsfeld, John, et al.
Published: (2023)
by: Lazarsfeld, John, et al.
Published: (2023)
Similar Items
-
MatryoshkaLoRA: Learning Accurate Hierarchical Low-Rank Representations for LLM Fine-Tuning
by: Modoranu, Ionut-Vlad, et al.
Published: (2026) -
The Iterative Optimal Brain Surgeon: Faster Sparse Recovery by Leveraging Second-Order Information
by: Wu, Diyuan, et al.
Published: (2024) -
FFT-based Dynamic Subspace Selection for Low-Rank Adaptive Optimization of Large Language Models
by: Modoranu, Ionut-Vlad, et al.
Published: (2025) -
LDAdam: Adaptive Optimization from Low-Dimensional Gradient Statistics
by: Robert, Thomas, et al.
Published: (2024) -
Unified Scaling Laws for Compressed Representations
by: Panferov, Andrei, et al.
Published: (2025)