Boundless Byte Pair Encoding: Breaking the Pre-tokenization Barrier
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Abstract (arXiv)
Pre-tokenization, the initial step in many modern tokenization pipelines, segments text into smaller units called pretokens, typically splitting on whitespace and punctuation. While this process encourages having full, individual words as tokens, it introduces a fundamental limitation in most tokenization algorithms such as Byte Pair Encoding (BPE). Specifically, pre-tokenization causes the distribution of tokens in a corpus to heavily skew towards common, full-length words. This skewed distribution limits the benefits of expanding to larger vocabularies, since the additional tokens appear with progressively lower counts. To overcome this barrier, we propose BoundlessBPE, a modified BPE algorithm that relaxes the pretoken boundary constraint. Our approach selectively merges two complete pretokens into a larger unit we term a superword. Superwords are not necessarily semantically cohesive. For example, the pretokens " of" and " the" might be combined to form the superword " of the". This merging strategy results in a substantially more uniform distribution of tokens across a corpus than standard BPE, and compresses text more effectively, with up to a 15% increase in bytes per token.
Key facts. Cited by 31 papers in the map (data through 7 Oct 2026). Builds on 22 of its 34 references that are in the map. First posted on arXiv 31 Mar 2025.
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22 of 34 references.
References · 22
- 2015Neural Machine Translation of Rare Words with Subword Unitscited by 2,898
- 2016Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translationcited by 1,471
- 2018Subword Regularization: Improving Neural Network Translation Models with Multiple Subword Candidatescited by 632
- 2020Byte Pair Encoding is Suboptimal for Language Model Pretrainingcited by 189
- 2024Tokenization counts: the impact of tokenization on arithmetic in frontier LLMscited by 112
- 2023Tokenizer Choice For LLM Training: Negligible or Crucial?cited by 105
- 2024Tokenization Is More Than Compressioncited by 87
- 2023The MiniPile Challenge for Data-Efficient Language Modelscited by 74
- 2024Getting the most out of your tokenizer for pre-training and domain adaptationcited by 65
- 2024Unpacking Tokenization: Evaluating Text Compression and its Correlation with Model Performancecited by 61
- 2025Over-Tokenized Transformer: Vocabulary is Generally Worth Scalingcited by 42
- 2024BPE Gets Picky: Efficient Vocabulary Refinement During Tokenizer Trainingcited by 30
10 more
- 2021Rare Tokens Degenerate All Tokens: Improving Neural Text Generation via Adaptive Gradient Gating for Rare Token Embeddingscited by 27
- 2024Egalitarian Language Representation in Language Models: It All Begins with Tokenizerscited by 20
- 2024Multi-word Tokenization for Sequence Compressioncited by 16
- 2024Scaffold-BPE: Enhancing Byte Pair Encoding for Large Language Models with Simple and Effective Scaffold Token Removalcited by 16
- 2025How Much is Enough? The Diminishing Returns of Tokenization Training Datacited by 15
- 2024Two Counterexamples to Tokenization and the Noiseless Channelcited by 13
- 2025Tokenization is Sensitive to Language Variationcited by 12
- 2023MiLe Loss: a New Entropy-Weighed Loss for Mitigating the Bias of Learning Difficulties in Large Language Modelscited by 8
- 2024An Analysis of BPE Vocabulary Trimming in Neural Machine Translationcited by 7
- 2024Don't Touch My Diacriticscited by 7
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Citing papers per year: 2025: 10; 2026: 21.
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- 2025Evaluating Morphological Alignment of Tokenizers in 70 Languagescited by 22
- 2025Broken Tokens? Your Language Model can Secretly Handle Non-Canonical Tokenizationscited by 16
- 2025BPE Stays on SCRIPT: Structured Encoding for Robust Multilingual Pretokenizationcited by 11
- 2025Exploiting Vocabulary Frequency Imbalance in Language Model Pre-trainingcited by 7
- 2025Explaining and Mitigating Crosslingual Tokenizer Inequitiescited by 7
- 2025Entropy-Driven Pre-Tokenization for Byte-Pair Encodingcited by 6
- 2026Stop Taking Tokenizers for Granted: They Are Core Design Decisions in Large Language Modelscited by 5
- 2026MinGram: A Minimalist Unigram Tokenizer with High Compression and Competitive Morphological Alignmentcited by 5
- 2025TokSuite: Measuring the Impact of Tokenizer Choice on Language Model Behaviorcited by 4
- 2026Tokenisation via Convex Relaxationscited by 4
- 2026Proxy Compression for Language Modelingcited by 3
- 2026LiteToken: Removing Intermediate Merge Residues From BPE Tokenizerscited by 3
19 more
- 2025SupraTok: Cross-Boundary Tokenization for Enhanced Language Model Performancecited by 2
- 2025TokDrift: When LLM Speaks in Subwords but Code Speaks in Grammarcited by 2
- 2025MUTANT: A Recipe for Multilingual Tokenizer Designcited by 2
- 2026Shorthand for Thought: Compressing LLM Reasoning via Entropy-Guided Supertokenscited by 2
- 2026Tokenization with Split Treescited by 2
- 2026TokEval: A Tokenizer Evaluation Suitecited by 2
- 2026Morphemes Without Borders: Evaluating Root-Pattern Morphology in Arabic Tokenizers and LLMscited by 1
- 2026Faster Superword Tokenizationcited by 1
- 2026Tokenizer-Agnostic Engram Modulecited by 1
- 2026HyperCLOVA X 32B Thinkcited by 0
- 2026Task-Centric Acceleration of Small-Language Modelscited by 0
- 2026From Where Words Come: Efficient Regularization of Code Tokenizers Through Source Attributioncited by 0
- 2026Check Your LLM's Secret Dictionary! Five Lines of Code Reveal What Your LLM Learned (Including What It Shouldn't Have)cited by 0
- 2026Adaptive Targeted Dynamic Chunking for Tokenization-Free Hierarchical Modelcited by 0
- 2026Where to cut, how deep: BPE and Unigram-LM on chemistry SMILEScited by 0
- 2026What Tokens are Learned when Tokenization is Optimized Jointly with Language Modeling?cited by 0
- 2026To Each Language Its Tokenizer: Modular Tokenizers for Efficient Multilingual LLMscited by 0
- 2026The Price of Token Boundaries: Compression Certificates and Predictioncited by 0
- 2026Counting and Min-Cost Encoding for Tokenization in Large Language Modelscited by 0
- 2026Counting and Min-Cost Encoding for Tokenization in Large Language Models1 Oct 2026
- 2026The Price of Token Boundaries: Compression Certificates and Prediction26 Sep 2026
- 2026To Each Language Its Tokenizer: Modular Tokenizers for Efficient Multilingual LLMs14 Sep 2026
- 2026TokEval: A Tokenizer Evaluation Suite18 Aug 2026
- 2026What Tokens are Learned when Tokenization is Optimized Jointly with Language Modeling?18 Aug 2026
- 2026Tokenizer-Agnostic Engram Module31 Jul 2026
- 2026Where to cut, how deep: BPE and Unigram-LM on chemistry SMILES6 Jul 2026
- 2026MinGram: A Minimalist Unigram Tokenizer with High Compression and Competitive Morphological Alignment25 Jun 2026
- 2026Adaptive Targeted Dynamic Chunking for Tokenization-Free Hierarchical Model28 May 2026
- 2026Tokenisation via Convex Relaxations21 May 2026
- 2026Tokenization with Split Trees21 May 2026
- 2026Check Your LLM's Secret Dictionary! Five Lines of Code Reveal What Your LLM Learned (Including What It Shouldn't Have)21 May 2026
19 more
- 2026Shorthand for Thought: Compressing LLM Reasoning via Entropy-Guided Supertokens29 Apr 2026
- 2026From Where Words Come: Efficient Regularization of Code Tokenizers Through Source Attribution15 Apr 2026
- 2026Faster Superword Tokenization6 Apr 2026
- 2026Morphemes Without Borders: Evaluating Root-Pattern Morphology in Arabic Tokenizers and LLMs16 Mar 2026
- 2026Task-Centric Acceleration of Small-Language Models27 Feb 2026
- 2026LiteToken: Removing Intermediate Merge Residues From BPE Tokenizers4 Feb 2026
- 2026Proxy Compression for Language Modeling4 Feb 2026
- 2026Stop Taking Tokenizers for Granted: They Are Core Design Decisions in Large Language Models19 Jan 2026
- 2026HyperCLOVA X 32B Think3 Jan 2026
- 2025TokSuite: Measuring the Impact of Tokenizer Choice on Language Model Behavior23 Dec 2025
- 2025MUTANT: A Recipe for Multilingual Tokenizer Design5 Nov 2025
- 2025Explaining and Mitigating Crosslingual Tokenizer Inequities24 Oct 2025
- 2025TokDrift: When LLM Speaks in Subwords but Code Speaks in Grammar16 Oct 2025
- 2025Exploiting Vocabulary Frequency Imbalance in Language Model Pre-training21 Aug 2025
- 2025SupraTok: Cross-Boundary Tokenization for Enhanced Language Model Performance16 Aug 2025
- 2025Evaluating Morphological Alignment of Tokenizers in 70 Languages8 Jul 2025
- 2025Broken Tokens? Your Language Model can Secretly Handle Non-Canonical Tokenizations23 Jun 2025
- 2025Entropy-Driven Pre-Tokenization for Byte-Pair Encoding18 Jun 2025
- 2025BPE Stays on SCRIPT: Structured Encoding for Robust Multilingual Pretokenization30 May 2025
- 2026Shorthand for Thought: Compressing LLM Reasoning via Entropy-Guided Supertokenscited by 2
- 2026HyperCLOVA X 32B Thinkcited by 0
- 2026Shorthand for Thought: Compressing LLM Reasoning via Entropy-Guided Supertokens29 Apr 2026
- 2026HyperCLOVA X 32B Think3 Jan 2026
- 2025Evaluating Morphological Alignment of Tokenizers in 70 Languagescited by 22
- 2025Broken Tokens? Your Language Model can Secretly Handle Non-Canonical Tokenizationscited by 16
- 2025BPE Stays on SCRIPT: Structured Encoding for Robust Multilingual Pretokenizationcited by 11
- 2025Exploiting Vocabulary Frequency Imbalance in Language Model Pre-trainingcited by 7
- 2025Explaining and Mitigating Crosslingual Tokenizer Inequitiescited by 7
- 2025Entropy-Driven Pre-Tokenization for Byte-Pair Encodingcited by 6
- 2026Stop Taking Tokenizers for Granted: They Are Core Design Decisions in Large Language Modelscited by 5
- 2026MinGram: A Minimalist Unigram Tokenizer with High Compression and Competitive Morphological Alignmentcited by 5
- 2025TokSuite: Measuring the Impact of Tokenizer Choice on Language Model Behaviorcited by 4
- 2026Tokenisation via Convex Relaxationscited by 4
- 2026Proxy Compression for Language Modelingcited by 3
- 2026LiteToken: Removing Intermediate Merge Residues From BPE Tokenizerscited by 3
12 most cited of 29 All 29 (JSON)
- 2026Counting and Min-Cost Encoding for Tokenization in Large Language Models1 Oct 2026
- 2026The Price of Token Boundaries: Compression Certificates and Prediction26 Sep 2026
- 2026To Each Language Its Tokenizer: Modular Tokenizers for Efficient Multilingual LLMs14 Sep 2026
- 2026TokEval: A Tokenizer Evaluation Suite18 Aug 2026
- 2026What Tokens are Learned when Tokenization is Optimized Jointly with Language Modeling?18 Aug 2026
- 2026Tokenizer-Agnostic Engram Module31 Jul 2026
- 2026Where to cut, how deep: BPE and Unigram-LM on chemistry SMILES6 Jul 2026
- 2026MinGram: A Minimalist Unigram Tokenizer with High Compression and Competitive Morphological Alignment25 Jun 2026
- 2026Adaptive Targeted Dynamic Chunking for Tokenization-Free Hierarchical Model28 May 2026
- 2026Tokenisation via Convex Relaxations21 May 2026
- 2026Tokenization with Split Trees21 May 2026
- 2026Check Your LLM's Secret Dictionary! Five Lines of Code Reveal What Your LLM Learned (Including What It Shouldn't Have)21 May 2026
12 newest of 29 All 29 (JSON)