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arXiv · 2506.15138

Less Is More: Reducing Token Counts Without Compromising Performance

Gyeongje Cho·Yeonkyoung So·Sangmin Lee·Jaejin Lee
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Abstract

Tokenization directly affects the inference efficiency of large language models, since fragmented tokenization increases sequence length and generation cost. Although longer, multi-word tokens can reduce fertility, naively adding them often degrades language model performance. We propose Thunder-Tok, a subword tokenizer that reduces fertility while preserving downstream performance. Thunder-Tok first constructs a large seed vocabulary from corpus substrings and filters structurally incomplete candidates, including invalid Unicode byte fragments and word-boundary violations. It then prunes the seed vocabulary using a likelihood-based token score derived from a uniform Jensen lower bound of the training-data probability. Experiments show that Thunder-Tok reduces fertility by approximately 25% in English and 9% in Korean compared with the standard BPE tokenizer while maintaining competitive performance.

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Gyeongje Cho, Yeonkyoung So, Sangmin Lee, Jaejin Lee. 2025-06-18. Less Is More: Reducing Token Counts Without Compromising Performance. https://arxiv.org/abs/2506.15138

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