Finding Optimal Tokenizers
A researcher explores a new algorithm for computing optimal tokenizers for large language models, noting that while theoretically intractable, it is solvable in practice. The author concludes that while the result is mathematically interesting, it offers limited practical utility compared to existing greedy methods like BPE.
Why it matters
Tokenization is a foundational component of LLM architecture; understanding its limits helps developers optimize model efficiency and performance.
In this post, I will present an algorithm that was able to compute an optimal tokenizer in some settings. This result is cool because optimal tokenization is theoretically intractable , but seems to be solvable in practice. My finding is very similar to various results on the Traveling Salesman Problem (TSP), where even difficult instances can be solved optimally using cutting-plane techniques.
The content is a technical exploration of computer science theory with no political or social bias.
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