MinGram: A Minimalist Unigram Tokenizer with High Compression and Competitive Morphological Alignment
The Unigram tokenizer uses an elegant representation which makes it straightforward to edit vocabularies, but its training is comparatively heavy and complex. We introduce MinGram (Minimalist Unigram), which keeps the token-list representation but simplifies training using a BPE-derived seed vocabulary, Hard EM on a minimum-token path, and a single flat score-pruning step. This removes the suffix array, the forward-backward pass, and the iterative prune loop, leaving a procedure that requires little beyond tokenizer inference itself. By making token count the primary objective and using a Unigram score only as a tiebreak, MinGram keeps the compression of pure token-count methods while ret...
| Authors | Sander Land |
|---|---|
| Primary category | cs.CL |
| Published | 25 June 2026 |
| Last updated at source | 12 August 2026 |
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