Instruction Distillation

Instruction distillation aims to transfer the capabilities of large language models (LLMs) to smaller, more efficient models by teaching them to follow instructions. Current research focuses on improving the quality and scalability of automatically generated instruction datasets, developing novel distillation techniques like program-aided distillation and sequential-metric based approaches, and applying these methods to diverse tasks including ranking, visual reasoning, and reinforcement learning. This research is significant because it addresses the limitations of LLMs' size and computational cost, enabling wider deployment and application in resource-constrained environments while potentially improving robustness and generalization.