Training Strategy

Training strategies in machine learning aim to optimize model performance and efficiency across diverse applications. Current research focuses on improving training speed and resource utilization, particularly for large language models and vision transformers, often employing techniques like curriculum learning, data augmentation, and model merging to enhance robustness and generalization. These advancements are crucial for deploying sophisticated AI models in resource-constrained environments (e.g., edge devices) and for addressing challenges in areas like medical image analysis and autonomous driving, where reliable and efficient performance is paramount.