In this episode we discuss LongNet: Scaling Transformers to 1,000,000,000 Tokens by Jiayu Ding, Shuming Ma, Li Dong, Xingxing Zhang, Shaohan Huang, Wenhui Wang, Nanning Zheng, Furu Wei. LONGNET is a new Transformer variant that allows for efficient processing of sequences over 1 billion tokens long using a novel dilated attention mechanism. This mechanism provides linear computational complexity and facilitates scaling, while maintaining performance on shorter sequences. The model is compatible with existing Transformer setups and has shown strong performance in tasks requiring long-sequence modeling and general language tasks, offering the potential to process vast text datasets as a single sequence.