# FLEXITOKENS: Flexible Tokenization for Evolving Language Models

arXiv: https://arxiv.org/abs/2507.12720 · HTML: https://mlsanity.com/paper/2507.12720/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 32 of its 50 references that are in the map.
- First posted on arXiv 17 Jul 2025.

## Builds on (most cited first, 30 of 32)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Neural Machine Translation of Rare Words with Subword Units](https://mlsanity.com/paper/1508.07909/) (2015)
- [No Language Left Behind: Scaling Human-Centered Machine Translation](https://mlsanity.com/paper/2207.04672/) (2022)
- [The FineWeb Datasets: Decanting the Web for the Finest Text Data at Scale](https://mlsanity.com/paper/2406.17557/) (2024)
- [XNLI: Evaluating Cross-lingual Sentence Representations](https://mlsanity.com/paper/1809.05053/) (2018)
- [Generating Sequences With Recurrent Neural Networks](https://mlsanity.com/paper/1308.0850/) (2013)
- [ByT5: Towards a token-free future with pre-trained byte-to-byte models](https://mlsanity.com/paper/2105.13626/) (2021)
- [CANINE: Pre-training an Efficient Tokenization-Free Encoder for Language Representation](https://mlsanity.com/paper/2103.06874/) (2021)
- [Language Model Tokenizers Introduce Unfairness Between Languages](https://mlsanity.com/paper/2305.15425/) (2023)
- [Do All Languages Cost the Same? Tokenization in the Era of Commercial Language Models](https://mlsanity.com/paper/2305.13707/) (2023)
- [MEGABYTE: Predicting Million-byte Sequences with Multiscale Transformers](https://mlsanity.com/paper/2305.07185/) (2023)
- [Charformer: Fast Character Transformers via Gradient-based Subword Tokenization](https://mlsanity.com/paper/2106.12672/) (2021)
- [SIB-200: A Simple, Inclusive, and Big Evaluation Dataset for Topic Classification in 200+ Languages and Dialects](https://mlsanity.com/paper/2309.07445/) (2023)
- [Adapting Pre-trained Language Models to African Languages via Multilingual Adaptive Fine-Tuning](https://mlsanity.com/paper/2204.06487/) (2022)
- [Byte Latent Transformer: Patches Scale Better Than Tokens](https://mlsanity.com/paper/2412.09871/) (2024)
- [Character-Level Language Modeling with Deeper Self-Attention](https://mlsanity.com/paper/1808.04444/) (2018)
- [Scaling Laws with Vocabulary: Larger Models Deserve Larger Vocabularies](https://mlsanity.com/paper/2407.13623/) (2024)
- [Tokenization counts: the impact of tokenization on arithmetic in frontier LLMs](https://mlsanity.com/paper/2402.14903/) (2024)
- [Tokenizer Choice For LLM Training: Negligible or Crucial?](https://mlsanity.com/paper/2310.08754/) (2023)
- [Hierarchical Transformers Are More Efficient Language Models](https://mlsanity.com/paper/2110.13711/) (2021)
- [WECHSEL: Effective initialization of subword embeddings for cross-lingual transfer of monolingual language models](https://mlsanity.com/paper/2112.06598/) (2021)
- [Coercing LLMs to do and reveal (almost) anything](https://mlsanity.com/paper/2402.14020/) (2024)
- [Efficient Transformers with Dynamic Token Pooling](https://mlsanity.com/paper/2211.09761/) (2022)
- [Dynamic Chunking for End-to-End Hierarchical Sequence Modeling](https://mlsanity.com/paper/2507.07955/) (2025)
- [MYTE: Morphology-Driven Byte Encoding for Better and Fairer Multilingual Language Modeling](https://mlsanity.com/paper/2403.10691/) (2024)
- [Evaluating Unsupervised Text Classification: Zero-shot and Similarity-based Approaches](https://mlsanity.com/paper/2211.16285/) (2022)
- [Efficient Domain Adaptation of Language Models via Adaptive Tokenization](https://mlsanity.com/paper/2109.07460/) (2021)
- [MAGNET: Improving the Multilingual Fairness of Language Models with Adaptive Gradient-Based Tokenization](https://mlsanity.com/paper/2407.08818/) (2024)
- [Task-Adaptive Tokenization: Enhancing Long-Form Text Generation Efficacy in Mental Health and Beyond](https://mlsanity.com/paper/2310.05317/) (2023)
- [Multilingual Pixel Representations for Translation and Effective Cross-lingual Transfer](https://mlsanity.com/paper/2305.14280/) (2023)

## Built on by (most cited first, 8 of 8)

- [The Token Tax: Systematic Bias in Multilingual Tokenization](https://mlsanity.com/paper/2509.05486/) (2025)
- [Continually Adding New Languages to Multilingual Language Models](https://mlsanity.com/paper/2509.11414/) (2025)
- [Compute Optimal Tokenization](https://mlsanity.com/paper/2605.01188/) (2026)
- [Proxy Compression for Language Modeling](https://mlsanity.com/paper/2602.04289/) (2026)
- [Phonemes to the Rescue: Multilingual Tokenization Based on International Phonetic Alphabet](https://mlsanity.com/paper/2606.20993/) (2026)
- [Scratchpad Patching: Decoupling Compute from Patch Size in Byte-Level Language Models](https://mlsanity.com/paper/2605.09630/) (2026)
- [ReconSpan: Reconstruction-Guided Adaptive Latent Tokenization](https://mlsanity.com/paper/2608.12756/) (2026)
- [Dynamic Multi-Byte Prediction With Hierarchical Language Models](https://mlsanity.com/paper/2608.15454/) (2026)

## Abstract (arXiv)

Adapting language models to new data distributions by simple finetuning is challenging. This is due to the rigidity of their subword tokenizers, which typically remain unchanged during adaptation. This inflexibility often leads to inefficient tokenization, causing overfragmentation of text in out-of-distribution domains, unseen languages, or scripts. In this work, we develop byte-level LMs with learnable tokenizers to make tokenization adaptive. Our models include a submodule that learns to predict boundaries given the input byte sequence, encoding it into variable-length segments. Most tokenizer-free methods train this boundary predictor using an auxiliary loss that enforces a fixed compression rate across the training corpus, introducing a new kind of rigidity. We propose FLEXITOKENS, a simplified training objective that enables significantly greater flexibility during adaptation. Evaluating across multiple multilingual benchmarks, morphologically diverse tasks, and domains, we demonstrate that FLEXITOKENS consistently reduces token over-fragmentation and achieves up to 10% point improvements on token classification and generative tasks compared to BPE and other gradient-based tokenizer baselines. We validate our findings using models of varying sizes, and our method demonstrates consistent improvements across scales. Code and data for our experiments will be released at https://github.com/skai-research/flexitokens