Cover Image

ConMeZO: Adaptive Descent-Direction Sampling for Gradient-Free Finetuning of Large Language Models

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Behric, Lejs Deen, Zhang, Liang, Li, Bingcong, Thekumparampil, Kiran Koshy
Format:	Preprint
Published:	2025
Subjects:	Machine Learning Optimization and Control
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

DPZero: Private Fine-Tuning of Language Models without Backpropagation
by: Zhang, Liang, et al.
Published: (2023)

Zeroth-Order Optimization Finds Flat Minima
by: Zhang, Liang, et al.
Published: (2025)

A Sinkhorn-type Algorithm for Constrained Optimal Transport
by: Tang, Xun, et al.
Published: (2024)

Accelerating Sinkhorn Algorithm with Sparse Newton Iterations
by: Tang, Xun, et al.
Published: (2024)

Comparing Few to Rank Many: Active Human Preference Learning using Randomized Frank-Wolfe
by: Thekumparampil, Kiran Koshy, et al.
Published: (2024)

Stochastic Adaptive Gradient Descent Without Descent
by: Aujol, Jean-François, et al.
Published: (2025)

Adaptive Conditional Gradient Descent
by: Khademi, Abbas, et al.
Published: (2025)

Stochastic Gradient Descent with Adaptive Data
by: Che, Ethan, et al.
Published: (2024)

The Sample Complexity of Gradient Descent in Stochastic Convex Optimization
by: Livni, Roi
Published: (2024)

Understanding the Implicit Regularization of Gradient Descent in Over-parameterized Models
by: Ma, Jianhao, et al.
Published: (2025)

Adaptive Step Sizes for Preconditioned Stochastic Gradient Descent
by: Köhne, Frederik, et al.
Published: (2023)

Corner Gradient Descent
by: Yarotsky, Dmitry
Published: (2025)

DoWG Unleashed: An Efficient Universal Parameter-Free Gradient Descent Method
by: Khaled, Ahmed, et al.
Published: (2023)

Anytime Acceleration of Gradient Descent
by: Zhang, Zihan, et al.
Published: (2024)

GeoAdaLer: Geometric Insights into Adaptive Stochastic Gradient Descent Algorithms
by: Eleh, Chinedu, et al.
Published: (2024)

A Local Polyak-Lojasiewicz and Descent Lemma of Gradient Descent For Overparametrized Linear Models
by: Xu, Ziqing, et al.
Published: (2025)

$k$-SVD with Gradient Descent
by: Jedra, Yassir, et al.
Published: (2025)

Scaling Laws for Gradient Descent and Sign Descent for Linear Bigram Models under Zipf's Law
by: Kunstner, Frederik, et al.
Published: (2025)

Directional Smoothness and Gradient Methods: Convergence and Adaptivity
by: Mishkin, Aaron, et al.
Published: (2024)

Stochastic Gradient Descent with Strategic Querying
by: Jiang, Nanfei, et al.
Published: (2025)

Unraveling the Gradient Descent Dynamics of Transformers
by: Song, Bingqing, et al.
Published: (2024)

Gradient Descent with Polyak's Momentum Finds Flatter Minima via Large Catapults
by: Phunyaphibarn, Prin, et al.
Published: (2023)

Gradient Descent on Logistic Regression with Non-Separable Data and Large Step Sizes
by: Meng, Si Yi, et al.
Published: (2024)

Gradient Descent on Logistic Regression: Do Large Step-Sizes Work with Data on the Sphere?
by: Meng, Si Yi, et al.
Published: (2025)

Learning Provably Improves the Convergence of Gradient Descent
by: Song, Qingyu, et al.
Published: (2025)

Mirror and Preconditioned Gradient Descent in Wasserstein Space
by: Bonet, Clément, et al.
Published: (2024)

Enhancing Fractional Gradient Descent with Learned Optimizers
by: Sobotka, Jan, et al.
Published: (2025)

Derivatives of Stochastic Gradient Descent in parametric optimization
by: Iutzeler, Franck, et al.
Published: (2024)

Convergence of Alternating Gradient Descent for Matrix Factorization
by: Ward, Rachel, et al.
Published: (2023)

On Penalty-based Bilevel Gradient Descent Method
by: Shen, Han, et al.
Published: (2023)

An Energy-Based Self-Adaptive Learning Rate for Stochastic Gradient Descent: Enhancing Unconstrained Optimization with VAV method
by: Zhang, Jiahao, et al.
Published: (2024)

Convergence Analysis of Stochastic Gradient Descent with MCMC Estimators
by: Li, Tianyou, et al.
Published: (2023)

On Gradient Descent Ascent for Nonconvex-Concave Minimax Problems
by: Lin, Tianyi, et al.
Published: (2019)

Gradient Descent's Last Iterate is Often (slightly) Suboptimal
by: Kornowski, Guy, et al.
Published: (2026)

Non-Euclidean Gradient Descent Operates at the Edge of Stability
by: Islamov, Rustem, et al.
Published: (2026)

Gauss-Newton Natural Gradient Descent for Shape Learning
by: King, James, et al.
Published: (2026)

Functional Central Limit Theorem for Stochastic Gradient Descent
by: Flamand, Kessang, et al.
Published: (2026)

On the Inherent Privacy of Zeroth Order Projected Gradient Descent
by: Gupta, Devansh, et al.
Published: (2025)

Open Problem: Anytime Convergence Rate of Gradient Descent
by: Kornowski, Guy, et al.
Published: (2024)

Parameter Symmetry and Noise Equilibrium of Stochastic Gradient Descent
by: Ziyin, Liu, et al.
Published: (2024)