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  "text": "Learning is Forgetting: LLM Training As Lossy Compression\n\n\"We argue LLMs are best seen as an instance of lossy compression, where  over training they learn by retaining only information in their training  data relevant to their objective(s). We show pre-training results in  models that are optimally compressed for next-sequence prediction,  approaching the Information Bottleneck bound on compression. Across an  array of open weights models, each compresses differently, likely due to  differences in the data and training recipes used.\"",
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