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New ToaST tokenizer cuts token counts by over 11%

By PulseAugur Editorial · [2 sources] ·

Researchers have developed a new subword tokenization method called Tokenization with Split Trees (ToaST). This method optimizes compression by recursively splitting text into binary trees and selecting vocabulary based on an Integer Program relaxation. ToaST has demonstrated an 11% reduction in token counts compared to existing methods like BPE and WordPiece, and improved performance in training 1.5B parameter language models. AI

IMPACT This new tokenization method could lead to more efficient language models by reducing token counts and extending effective context length.

RANK_REASON The cluster contains an academic paper detailing a new method for subword tokenization.

Read on arXiv cs.CL →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

COVERAGE [2]

  1. arXiv cs.CL TIER_1 English(EN) · Craig W. Schmidt, Michael Krumdick, Adam Wiemerslage, Seth Ebner, Varshini Reddy, Yuval Pinter, Chris Tanner ·

    Tokenization with Split Trees

    arXiv:2605.22705v1 Announce Type: new Abstract: We introduce Tokenization with Split Trees (ToaST), a subword tokenization method that directly optimizes compression under a new recursive inference procedure. ToaST greedily splits each pretoken into a full binary tree using preco…

  2. arXiv cs.CL TIER_1 English(EN) · Chris Tanner ·

    Tokenization with Split Trees

    We introduce Tokenization with Split Trees (ToaST), a subword tokenization method that directly optimizes compression under a new recursive inference procedure. ToaST greedily splits each pretoken into a full binary tree using precomputed byte n-gram counts, independent of any vo…

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