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end-to-end learning

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papersTODAY 04:00 UTC

arXiv paper proposes learning tokenization end-to-end via reinforcement learning

A new arXiv preprint argues that tokenization remains a fixed, hand-designed compression step in large language model pipelines even as other components become trainable end-to-end. The authors report that reinforcement learning can be used to learn tokenization jointly with the model, with earlier work showing promise at scale. The paper appears as a cross-listed replacement submission in cs.AI and cs.LG.

tokenization/reinforcement-learning/llm/arxiv