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Byte Latent Transformer: Patches Scale Better Than Tokens

Pagnoni et al., 2025, Byte Latent Transformer (Proceedings of the 63rd Annual Meeting of ACL (Volume 1: Long Papers))

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2.1 Tokenization vs. Patching

  1. How do “patches” in BLT differ from “tokens”?
    Tokens come from a finite vocabulary fixed before training; patches are byte groups formed dynamically with no vocabulary, so any byte sequence can be a patch and the model still sees the bytes inside it, e.g. spelling.
    • Pagnoni et al., 2025, Byte Latent Transformer (Proceedings of the 63rd Annual Meeting of ACL (Volume 1: Long Papers)), §2.1, p. 9239

Source

  • Artidoro Pagnoni, Ramakanth Pasunuru, Pedro Rodriguez, John Nguyen, Benjamin Muller, Margaret Li, Chunting Zhou, Lili Yu, Jason E. Weston, Luke Zettlemoyer, Gargi Ghosh, Mike Lewis, Ari Holtzman and Srini Iyer. 2025. Byte Latent Transformer: Patches Scale Better Than Tokens. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 9238–9258. License: CC BY 4.0. Flashcards adapted from this work. doi:10.18653/v1/2025.acl-long.453 https://aclanthology.org/2025.acl-long.453/