Intermediate Checkpoint

Intermediate checkpoints, saved model weights during training, are increasingly central to improving the efficiency and performance of large-scale machine learning models. Research focuses on optimizing checkpoint usage for fault tolerance in distributed training (especially for Mixture-of-Experts models), enhancing model quality through checkpoint averaging and Bayesian optimization techniques, and reducing training costs by leveraging information from multiple checkpoints. These advancements are crucial for mitigating the high computational demands of training large language models and other complex architectures, leading to more efficient and robust AI systems.