Paper: [ Ссылка ]
The paper introduces the Byte Latent Transformer (BLT), a novel large language model architecture that processes raw byte data without tokenization. BLT dynamically groups bytes into patches based on predicted entropy, allocating more computational resources to complex sections of text. This approach achieves performance comparable to tokenization-based models while significantly improving inference efficiency and robustness to noisy input. The authors present a scaling study demonstrating BLT's superior scaling properties and its enhanced performance on various downstream tasks, particularly those requiring sub-word understanding. Finally, the study explores methods to leverage pre-trained models to improve BLT training.
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