{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/cola-compute-efficient-pre-training-of-llms","title":"CoLA: Compute-Efficient Pre-Training of LLMs via Low-Rank Activation","arxiv_id":"2502.10940","date":"2025-02-16","proceeding":null,"authors":["Ziyue Liu","Ruijie Zhang","Zhengyang Wang","Zi Yang","Paul Hovland","Bogdan Nicolae","Franck Cappello","Zheng Zhang"],"abstract":"The full-size MLPs and the projection layers in attention introduce tremendous model sizes of large language models (LLMs), imposing extremely demanding needs of computational resources in the pre-training stage. However, we empirically observe that the activations of pre-trained LLMs exhibit low-rank property. Motivated by such observations, we propose CoLA and its memory-efficient implementation, CoLA-M, to replace these full-size layers with compute-efficient auto-encoders that naturally enforce low-rank activations throughout training. This fundamental architectural change eliminates the activation redundancy and significantly boosts model capacity and training efficiency. Experiments on LLaMA models with 60 million to 7 billion parameters show that CoLA reduces the computing cost by $\\bf 2\\pmb{\\times}$ and improves training throughput by $\\bf 1.86\\pmb{\\times}$ while maintaining full-rank level performance. CoLA-M further squeezes memory cost without sacrificing throughput, offering a pre-training approach with collectively superior parameter, computing, and memory efficiency. The LLMs produced are also $\\bf 2\\pmb{\\times}$ smaller, enabling faster inference with lower memory cost on resource-constrained platforms.","url_abs":"https://arxiv.org/abs/2502.10940v2","url_pdf":"https://arxiv.org/pdf/2502.10940v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"cola-compute-efficient-pre-training-of-llms","repo_url":"https://github.com/alvin-zyl/cola","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"ok"}}],"tasks":[{"task_slug":null,"task_name":"CoLA"}],"methods":[{"method_slug":"attention","method_name":"Attention"},{"method_slug":"cola","method_name":"COLA"},{"method_slug":"llama","method_name":"LLaMA"},{"method_slug":"softmax","method_name":"Softmax"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2502.10940","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}