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A Distribution-Matching Perspective on LLM Training Algorithms
In this work, we propose a unified framework for training algorithms across different stages of modern large language model development, based on the perspective of distribution matching. We show that a broad class of language model training algorithms can be interpreted as minimizing a certain dive
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