Alignment Distillation

Alignment distillation is a machine learning technique focused on transferring knowledge from a large, complex "teacher" model to a smaller, more efficient "student" model, aiming to maintain or even improve performance while reducing computational costs. Current research emphasizes improving the distillation process through selective data sampling (e.g., focusing on informative negative examples), refined alignment strategies (e.g., feature-level or partial alignment), and the development of novel architectures tailored for specific tasks (e.g., object detection, language modeling, and multimodal learning). This technique is significant for deploying advanced models on resource-constrained devices and improving the robustness and security of large language models, impacting both research efficiency and practical applications.