{"id":178984,"date":"2023-12-24T00:41:17","date_gmt":"2023-12-24T06:41:17","guid":{"rendered":"https:\/\/lifeboat.com\/blog\/2023\/12\/can-we-train-massive-neural-networks-more-efficiently-meet-relora-the-game-changer-in-ai-training"},"modified":"2023-12-24T00:41:17","modified_gmt":"2023-12-24T06:41:17","slug":"can-we-train-massive-neural-networks-more-efficiently-meet-relora-the-game-changer-in-ai-training","status":"publish","type":"post","link":"https:\/\/lifeboat.com\/blog\/2023\/12\/can-we-train-massive-neural-networks-more-efficiently-meet-relora-the-game-changer-in-ai-training","title":{"rendered":"Can We Train Massive Neural Networks More Efficiently? Meet ReLoRA: the Game-Changer in AI Training"},"content":{"rendered":"<p><a class=\"aligncenter blog-photo\" href=\"https:\/\/lifeboat.com\/blog.images\/can-we-train-massive-neural-networks-more-efficiently-meet-relora-the-game-changer-in-ai-training3.jpg\"><\/a><\/p>\n<p>In machine learning, larger networks with increasing parameters are being trained. However, training such networks has become prohibitively expensive. Despite the success of this approach, there needs to be a greater understanding of why overparameterized models are necessary. The costs associated with training these models continue to rise exponentially.<\/p>\n<p>A team of researchers from the University of Massachusetts Lowell, Eleuther AI, and Amazon developed a method known as ReLoRA, which uses low-rank updates to train high-rank networks. ReLoRA accomplishes a high-rank update, delivering a performance akin to conventional neural network training.<\/p>\n<p>Scaling laws have been identified, demonstrating a strong power-law dependence between network size and performance across different modalities, supporting overparameterization and resource-intensive neural networks. The Lottery Ticket Hypothesis suggests that overparameterization can be minimized, providing an alternative perspective. Low-rank fine-tuning methods, such as LoRA and Compacter, have been developed to address the limitations of low-rank matrix factorization approaches.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>In machine learning, larger networks with increasing parameters are being trained. However, training such networks has become prohibitively expensive. Despite the success of this approach, there needs to be a greater understanding of why overparameterized models are necessary. The costs associated with training these models continue to rise exponentially. A team of researchers from the [\u2026]<\/p>\n","protected":false},"author":661,"featured_media":0,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[6,1491],"tags":[],"class_list":["post-178984","post","type-post","status-publish","format-standard","hentry","category-robotics-ai","category-transportation"],"_links":{"self":[{"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/posts\/178984","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/users\/661"}],"replies":[{"embeddable":true,"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/comments?post=178984"}],"version-history":[{"count":0,"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/posts\/178984\/revisions"}],"wp:attachment":[{"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/media?parent=178984"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/categories?post=178984"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/tags?post=178984"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}