What is STRR and how does it reveal poor tokenizer coverage that fertility alone misses?

**STRR**, the single-token retention rate, is the fraction of a language's words that the tokenizer keeps as one token rather than splitting. If 30 out of every 100 distinct words are single tokens, the STRR is 0.3. Fertility is different. It is the average number of tokens per word measured over a real text sample. Because common words appear far more often than rare ones, fertility is dominated by the most frequent words. Here is the scenario that shows why this matters. Suppose a language has 50 very common words, each a single token, and 5,000 less-common words that each split into four or five tokens. If common words make up 80 percent of a test corpus, the average fertility comes out around 1.7, which looks fine. But STRR over the full 5,050-word vocabulary is about 1 percent, because almost no words got single-token coverage. So a user who writes about ordinary topics sees a low cost, while a user writing about anything specific pays the full fragmentation penalty. Fertility tells you the typical cost; STRR tells you how broadly the tokenizer covers the language. You need both to judge fairness honestly.

**STRR**, the single-token retention rate, is the fraction of a language's vocabulary that maps to exactly one token. The numerator counts words tokenized as one token; the denominator is the total distinct words. The difference from fertility is weighting. Fertility is a corpus average, so it is frequency-weighted and the most common words dominate. STRR is computed over the distinct word list, so every word counts once. Consider a language with 50 very common single-token words and 5,000 rare words that each fragment into 4.5 tokens. If common words are 80 percent of a corpus, fertility is about 0.8 times 1 plus 0.2 times 4.5, roughly 1.7, which reads as acceptable. STRR, though, is 50 over 5,050, about 1 percent. The average looked fine while almost the entire vocabulary fragments. The reason both are necessary is that they answer different questions. Fertility answers what typical text costs; STRR answers how broadly the tokenizer covers the language. A few common words can hold the average down while the meaningful tail, named entities and technical terms, shatters. Reporting fertility and STRR together exposes both the average cost and the coverage gap, which is the honest fairness picture.