Low Precision Training

Low-precision training aims to accelerate and reduce the memory footprint of deep learning model training by using lower-precision numerical representations (e.g., FP8, INT4) for weights, activations, and gradients. Current research focuses on improving the stability and efficiency of these methods across various architectures, including large language models (LLMs) and vision-language models, often employing techniques like dynamic precision scheduling and novel optimization algorithms (e.g., variations of AdamW). This research is significant because it directly addresses the high computational cost of training large models, potentially enabling faster development and deployment of advanced AI systems across diverse applications with limited resources.