# A second-order-like optimizer with adaptive gradient scaling for deep learning

Source: [A second-order-like optimizer with adaptive gradient scaling for deep learning](<https://www.sota2.com/research/paper/68fa5cb31e209131ce9d383a>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T17:48:19.946Z

Authors: J\\'er\\^ome Bolte, Ryan Boustany, Edouard Pauwels, Andrei Purica

Published: 2024-10-08T09:58:38.000Z

## Abstract

In this empirical article, we introduce INNAprop, an optimization algorithm that combines the INNA method with the RMSprop adaptive gradient scaling. It leverages second-order information and rescaling while keeping the memory requirements of standard DL methods as AdamW or SGD with momentum. After giving geometrical insights, we evaluate INNAprop on CIFAR-10, Food101, and ImageNet with ResNets, VGG, DenseNet, and ViT, and on GPT-2 (OpenWebText) train from scratch and with LoRA fine-tuning (E2E). INNAprop consistently matches or outperforms AdamW both in training speed and accuracy, with minimal hyperparameter tuning in large-scale settings. Our code is publicly available at \\url{https://github.com/innaprop/innaprop}.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2410.05871>)

[PDF](<https://arxiv.org/pdf/2410.05871.pdf>)