Autoregressive Distillation

Autoregressive distillation focuses on improving the efficiency and performance of autoregressive models, which generate sequential outputs like text or solutions to optimization problems. Current research explores techniques like knowledge distillation to transfer knowledge from large, computationally expensive autoregressive models to smaller, faster non-autoregressive counterparts, or to create synthetic datasets (Farzi Data) that retain model performance while drastically reducing training data size. These advancements aim to reduce the computational burden of training and inference, enabling the development of more efficient and scalable large language models and other sequential prediction systems, with applications ranging from natural language processing to operations research.