Low-Precision Training of Large Language Models: Methods, Challenges, and Opportunities

Abstract:Large language models (LLMs) have achieved impressive performance across various domains. However, the substantial hardware resources required for their training present a significant barrier to efficiency and scalability. To mitigate this challenge, low-precision training techniques have been widely adopted, leading to notable advancements in training efficiency. Despite these gains, low-precision training involves several components, such as weights, activations, and gradients, each of which can be represented in different numerical formats. The resulting diversity has created a fragmented landscape in low-precision training research, making it difficult for researchers to gain a unified overview of the field. This survey provides a comprehensive review of existing low-precision training methods. To systematically organize these approaches, we categorize them into three primary groups based on their underlying numerical formats, which is a key factor influencing hardware compatibility, computational efficiency, and ease of reference for readers. The categories are (1) fixed-point and integer-based methods, (2) floating-point-based methods, and (3) customized format-based methods. Additionally, we discuss quantization-aware training approaches, which share key similarities with low-precision training during forward propagation. Beyond efficiency, we examine robustness and deployment reliability under low precision. Finally, we highlight several promising research directions to advance this field. A collection of papers discussed in this survey is provided in this https URL.

Machine Learning (cs.LG); Artificial Intelligence (cs.AI)

arXiv:2505.01043 [cs.LG]

(or arXiv:2505.01043v2 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2505.01043 Focus to learn more arXiv-issued DOI via DataCite

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