Parallel Self Distillation

Parallel self-distillation is a model compression technique aiming to train smaller, more efficient "student" models that mimic the performance of larger, more resource-intensive "teacher" models. Current research focuses on improving distillation methods by incorporating diverse training signals, such as chain-of-thought rationales, program-of-thought prompts, and even gradient information, to enhance student model reasoning and robustness. This approach holds significant promise for deploying advanced models in resource-constrained environments and improving the efficiency of various machine learning tasks, including natural language processing, image classification, and point cloud processing.