June 11, 2026
Token drama: optimized to death
Finding Optimal Tokenizers
A math wizard may have cracked a “best possible” word-splitting trick — and commenters are fighting over whether anyone should care
TLDR: A researcher found a way to compute a theoretically “best” text-splitting system for AI in some cases, even though most experts thought that was too hard to do. Commenters were torn between calling it a beautiful math flex and roasting it as a heroic effort to improve something that was already basically fine.
A researcher dropped a brainy new post claiming they can find the best possible way to chop text into the little pieces AI models read — and the community reaction was basically: "That’s amazing," "That’s useless," and "Why was this so fun to read?" In plain English, this is about helping AI pack words more efficiently before training. The twist? The author openly admits the breakthrough may not matter much in real life, because today’s methods are already very close and you can often just make the word list a bit bigger and move on. Naturally, commenters pounced on that honesty.
The strongest split was between the "this is elegant science" crowd and the "congrats on optimizing the last 1%" crowd. Fans compared it to solving a famously hard puzzle just because it’s there, cheering the sheer nerd audacity. Skeptics rolled their eyes and said this is peak tech culture: enormous mathematical effort to maybe save a tiny amount. Others jumped into a side-drama about whether “optimal” on old data means anything if it flops on new text, which turned into the classic internet fight between beautiful theory and messy reality.
And yes, the jokes flew. People meme’d about spending supercomputer energy to save a handful of tokens, while others treated the whole thing like the AI version of shaving milliseconds off a race car that’s already winning. Even the article’s humble tone got applause, with commenters calling it weirdly refreshing to see a researcher say, essentially, "I did this because it was cool". For once, the comments weren’t just dunking — they were enjoying the spectacle
Key Points
- •The article presents an algorithm that computes an optimal tokenizer in some settings despite the problem being theoretically intractable.
- •The author says the practical usefulness may be limited because existing tokenizers are often already close to optimal, sometimes within about 1%.
- •Tokenizers for LLMs are fixed vocabularies that map integer tokens to byte sequences and are chosen to compress training data under a vocabulary-size constraint.
- •The article describes a formulation from Tempus et al. that models tokenization as an integer linear program with vocabulary-selection and token-use variables.
- •The ILP uses constraints linking token usage to vocabulary membership and flow constraints to ensure exactly one valid tokenization across the dataset.