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新论文 · v1arXiv:2611.08671

Greedy Byte-Pair Encoding Achieves at Least 0.361 of the Optimal Compression

Kundan Baliga, Aiden Xie, Aditya Byju

原文摘要

Byte-Pair Encoding (BPE) builds a tokeniser by repeatedly merging a most frequent adjacent pair of symbols; its utility is the total reduction in string length achieved by $k$ merges. It was known that this greedy rule achieves at least a third of the optimal reduction. We prove that it always achieves at least 0.361 of the optimal reduction in string length, for every string, every budget and every tie-breaking rule; this is the first improvement of the bound one third. We also explain why one third was a barrier: the pair-packing relaxation behind it is tight, so a better bound must use more of the optimum's structure. When greedy destroys three of the optimum's merge points at once, one of them can usually be moved next door, because all copies of a rule of the optimum look alike; the remaining cases are paid for later. Along the way we determine the worst-case ratio for two merges exactly, bound it for three, and isolate a single inequality that would settle the problem.

cs.DS · cs.CL

新论文 · v1arXiv:2611.07346

GreedTok is a Constant-Factor Approximation for Partition-Cover Tokenization

Kundan Baliga, Aiden Xie, Aditya Byju

原文摘要

GreedTok (Lim, Tan, Choo and Lauw, NeurIPS 2025) builds a tokenizer vocabulary greedily for the partition-cover objective: choose $k$ tokens so as to maximize the number of adjacent character pairs merged when the corpus is segmented optimally. The objective is neither submodular nor supermodular, so the classical $1-1/e$ analysis does not apply, and a worst-case guarantee was left open. We prove that GreedTok is a constant-factor approximation: on every corpus and for every budget $k$ it achieves at least $0.2752$ of the optimum, and it is optimal for $k=1$. The bound holds for both the exact greedy loop and the lazy priority-queue loop of the published algorithm and its code. The key ingredient is a shift lemma: if two occurrences of a not-yet-chosen token overlap and both are still usable, they offer exactly the same gain, so GreedTok's left-to-right scoring loses nothing when occurrences collide. A per-occurrence charging argument, with window distributions certified in exact arithmetic or given in closed form, completes the proof. In the other direction, we construct corpora on which GreedTok, with adversarial tie-breaking, achieves at most a fraction of the optimum tending to $(7-\ln 2)/12\approx0.5256$ (below $0.5380$ when every greedy choice is unique). Finally, we show that the objective is NP-hard to approximate within $1-1/31580$, to our knowledge the first inapproximability result for it.

cs.DS