arXiv AI July 7, 2026 By Siyuan Li, Jiabao Pan, Yumou Liu, Zhuoli Ouyang, Xin Jin, Xinglong Xu, Jingxuan Wei, Shengye Pang, Jintao Che, Xuanhe Zhou, Conghui He, Cheng Tan

OmniOpt: Taxonomy, Geometry, and Benchmarking of Modern Optimizers

Read the original on arXiv AI →

arXiv:2607. 04033v1 Announce Type: cross Abstract: Optimizer selection for large-scale model training has become a system-level design decision constrained jointly by compute, memory, tuning budget, and task diversity, yet the landscape of over one hundred methods remains fragmented.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

arXiv Machine Learning

Jul 9

Efficient Long-Horizon Learning for Learned Optimization

arXiv:2607. 06772v1 Announce Type: new Abstract: Learned optimization aims to improve upon hand-designed optimizers (e.

By Xiaolong Huang, Benjamin Th\'erien, James Harrison, Eugene Belilovsky

llms computer-vision

arXiv Machine Learning

Jun 16

Zero-order Parameter-free Optimization for LMO-based Methods: Novel Approach for Efficient Fine-tuning

arXiv:2606. 14970v1 Announce Type: new Abstract: Fine-tuning large language models (LLMs) has become a central application of modern optimization, enabling pretrained models to adapt to diverse downstream tasks and domain-specific data.

By Dmitriy Bystrov, Daniil Medyakov, Dmitry Bylinkin, Aleksandr Beznosikov

llms fine-tuning

Hugging Face Trending Papers

Jul 7

Efficient Long-Horizon Learning for Learned Optimization

Learned optimization aims to improve upon hand-designed optimizers (e. g.

llms computer-vision

arXiv AI

Aug 6

ORACLE: A Multi-Objective Reinforcement Learning-Based Analog Circuit Design Optimizer with Large Language Models-Guided Exploration

arXiv:2608. 04999v1 Announce Type: cross Abstract: Analog circuit design automation using reinforcement learning (RL) has emerged as a promising approach for reducing manual effort.

By Osei Brempong, Mohammed Ayman Habib, Vivan Poddar, Morteza Fayazi

llms reinforcement-learning benchmarks

arXiv Machine Learning

Sep 1

MOONSHOT : A Framework for Multi-Objective Pruning of Vision and Large Language Models

arXiv:2604.13287v2 Announce Type: replace Abstract: Weight pruning is a common technique for compressing large neural networks. We focus on the challenging post-training one-shot setting, where a pre...

By Gabriel Afriat, Xiang Meng, Shibal Ibrahim, Hussein Hazimeh, Rahul Mazumder

llms efficiency benchmarks

arXiv Machine Learning

Jun 2

OP-LoRA: The Blessing of Dimensionality

arXiv:2412. 10362v2 Announce Type: replace Abstract: Low-rank adapters (LoRA) enable finetuning of large models with only a small number of parameters.

By Piotr Teterwak, Kate Saenko, Bryan A. Plummer, Ser-Nam Lim

diffusion fine-tuning

One email a morning, machine-written

Related stories

Efficient Long-Horizon Learning for Learned Optimization

Zero-order Parameter-free Optimization for LMO-based Methods: Novel Approach for Efficient Fine-tuning

Efficient Long-Horizon Learning for Learned Optimization

ORACLE: A Multi-Objective Reinforcement Learning-Based Analog Circuit Design Optimizer with Large Language Models-Guided Exploration

MOONSHOT : A Framework for Multi-Objective Pruning of Vision and Large Language Models

OP-LoRA: The Blessing of Dimensionality