arXiv:2409.04777v4 Announce Type: replace Abstract: Large Language Models have driven significant AI advancements, yet their training is resource-intensive and highly sensitive to hyper-parameter selection. While scaling laws provide valuable guidance on model size and data requirements, they fall short in choosing dynamic hyper-parameters, such as learning-rate (LR) schedules, that evolve during training. To bridge this gap, we present Optimization Hyper-parameter Laws (Opt-Laws), a framework that predicts final training loss as a function of LR schedule, model size, and data size. Grounded i
The increasing scale and resource intensity of Large Language Model training necessitates more efficient optimization methods, pushing researchers to develop frameworks like Opt-Laws.
This research provides a framework to predict optimal hyper-parameters for Training Large Language Models, potentially reducing the immense computational resources and time currently consumed by trial-and-error.
The ability to predict optimal learning rate schedules and other hyper-parameters will make LLM training more efficient, predictable, and accessible, potentially accelerating AI development.
- · AI researchers
- · Cloud providers with GPUs
- · Companies developing LLMs
- · Researchers reliant on brute-force hyper-parameter search
Reduced computational costs and time for training large language models.
Faster iteration cycles in AI development, leading to quicker advancements and new applications.
Enhanced competition in the AI landscape as smaller players can more efficiently train competitive models.
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Read at arXiv cs.LG