Where to cut, how deep: BPE and Unigram-LM on chemistry SMILES

Every chemical language model reading SMILES begins with a tokenizer, yet the field has inherited byte-pair encoding (BPE) from natural language with little scrutiny.

In natural language, BPE's principal alternative, Unigram-LM, is known to build structurally different vocabularies.

Whether that contrast survives in chemistry was open.

We report a controlled comparison of BPE and Unigram-LM over a fixed 165-token chemistry base, at the small vocabulary sizes where token embeddings are learnable, across three corpus typologies (diverse, drug-like, natural-products) and both pre-tokenization boundary policies.

The two do not converge.

In all 22 matched conditions they build near-disjoint subword vocabularies: cross-algorithm Jaccard overlap on the learned pieces never exceeds 0.161, and at most 0.05 once weighted toward the high-frequency pieces a model updates most.

Unigram-LM also segments held-out molecules into 29-41% more tokens; the arms largely agree on where to cut but not how deeply, so BPE's segmentation is a strict coarsening of Unigram-LM's on 80-99% of molecules.

The separation holds across corpus, boundary, and vocabulary size, persisting even at eight times that scale.

The subword algorithm is therefore a modeling decision, not a free default.

The study trains no language models.