Scaling Law

Scaling laws in machine learning aim to quantify the relationship between a model's performance and factors like its size, training data volume, and computational resources. Current research focuses on refining these laws across diverse model architectures, including transformers (both encoder-decoder and decoder-only), and optimization algorithms like SGD and AdamW, investigating their applicability to various tasks such as language modeling, translation, and image classification. Understanding these scaling laws is crucial for optimizing resource allocation in model development, improving training efficiency, and guiding the design of future, more powerful AI systems. Furthermore, the principles are being extended to explore economic productivity and the impact of data quality.