Home AI News Training Over-Parametrized Models: The Need, Challenges, and Low-Rank Training Approaches

Training Over-Parametrized Models: The Need, Challenges, and Low-Rank Training Approaches

Training Over-Parametrized Models: The Need, Challenges, and Low-Rank Training Approaches

AI Training Strategies: The Importance of Over-Parametrized Models

In the field of machine learning, the practice of training larger and more over-parametrized networks has become common in the last decade. However, the cost associated with training such networks has hindered their widespread adoption. Despite this, there is still a lack of theoretical understanding of why it is necessary to train models with significantly more parameters than the training instances.

Various alternative approaches to scaling, such as compute-efficient scaling optima and retrieval-augmented models, have been explored. While these approaches offer interesting trade-offs, they do not address the need for training over-parametrized models in a more accessible and comprehensible way.

Recent studies have shown that overparametrization is not always required for training. The Lottery Ticket Hypothesis suggests that during initialization or early training, there may be isolated sub-networks (winning tickets) that can achieve the performance of the entire network when trained.

To address this issue, researchers at the University of Massachusetts Lowell have introduced a new approach called ReLoRA. ReLoRA utilizes the rank of sum property to train a high-rank network with a series of low-rank updates. Their findings demonstrate that ReLoRA can achieve comparable results to standard neural network training. By incorporating a merge-and-rein-it approach, a jagged learning rate scheduler, and partial optimizer resets, ReLoRA’s efficiency is improved, especially in large networks.

The researchers conducted tests on 350M-parameter transformer language models, focusing on autoregressive language modeling. The results show that ReLoRA’s effectiveness increases with model size, suggesting its potential for training networks with billions of parameters.

Training big language models and neural networks can benefit greatly from low-rank training approaches. These approaches offer promising opportunities to enhance training efficiency and deepen our understanding of neural networks and their generalization capabilities in the over-parametrized domain. By further exploring low-rank training, the community can contribute significantly to the development of deep learning theories.

To learn more about ReLoRA and its implementation, you can refer to the paper and GitHub link provided.

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