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Objective vs. Search: Decomposing What Makes a Good Tokeniser

Paper recorded by Signals 4 on 2026-09-16 in cs.AI. Abstract reproduced from arXiv; link to the original below.

Published 2026-09-16 on arXiv · recorded by Signals 4 on 2026-09-17

Category: cs.AI · 人工智能 · first seen 2026-09-17

Abstract

Two dominant tokenisation algorithms are used by modern language models: byte-pair encoding (BPE) and UnigramLM. These differ along two orthogonal axes: their optimisation objective (compression vs. log-likelihood) and their search procedure (bottom-up merging vs. top-down pruning). Existing comparisons confound these axes, making it unclear whether their observed differences stem from what is bei

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#21 most recent of 300 cs.AI papers we have recorded · ↑ newer: PAA: The Probabilistic Allen Algebra: A Generative and Complete Probab · ↓ older: A Zeroth-Order Paradigm for LLM Preference Alignment
Cite this page: Objective vs. Search: Decomposing What Makes a Good Tokeniser: the #21 most recent of 300 cs.AI papers we have recorded (as of 2026-09-16). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/objective-vs-search-decomposing-what-makes-a-good-tokeniser.html
Free to quote with attribution to “Signals 4 (Signals API)”. Machine-readable: papers.json
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