This episode analyzes the research paper titled **"Byte Latent Transformer: Patches Scale Better Than Tokens,"** authored by Artidoro Pagnoni, Ram Pasunuru, Pedro Rodriguez, John Nguyen, Benjamin Muller, Margaret Li, Chunting Zhou, Lili Yu, Jason Weston, Luke Zettlemoyer, Gargi Ghosh, Mike Lewis, Ari Holtzman, and Srinivasan Iyer from FAIR at Meta, the Paul G. Allen School of Computer Science & Engineering at the University of Washington, and the University of Chicago. The discussion explores the innovative Byte Latent Transformer (BLT) architecture, which diverges from traditional tokenization by utilizing dynamically sized byte patches based on data entropy. This approach enhances model efficiency and scalability, allowing BLT to match the performance of established models like Llama 3 while reducing computational costs by up to 50% during inference. Additionally, the episode examines BLT’s improvements in handling noisy inputs, character-level understanding, and its ability to scale both model and patch sizes within a fixed inference budget, highlighting its significance in advancing large language model technology.

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For more information on content and research relating to this episode please see: https://dl.fbaipublicfiles.com/blt/BLT__Patches_Scale_Better_Than_Tokens.pdf