Training Compute

Training compute, the computational resources required to train machine learning models, is a critical factor determining model performance and capabilities. Current research focuses on optimizing training efficiency through techniques like improved training schedules, sparse model architectures (e.g., Mixture-of-Experts), and self-training methods that reduce reliance on large labeled datasets. These advancements aim to reduce the substantial computational cost associated with training large language models and other complex AI systems, impacting both the economic feasibility and environmental sustainability of AI development. The efficient use of training compute is also crucial for regulatory oversight, as it correlates with model capabilities and potential risks.