You Can Learn Tokenization End-to-End with Reinforcement Learning

arXiv:2602.13940v2 Announce Type: replace Abstract: Tokenization is a hardcoded compression step which remains in the training pipeline of Large Language Models (LLMs), despite a general trend towards architectures becoming increasingly end-to-end. Prior work has shown promising results at scale in bringing this compression step inside the LLMs' architecture with heuristics to draw token boundaries, and also attempts to learn these token boundaries with straight-through estimates, which treat the problem of drawing discrete token boundaries as a continuous one. We show that these token boundar

Source: arXiv cs.LG — read the full report at the original publisher.

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