Learning Is Forgetting; LLM Training as Lossy Compression
SMRTR summary
Large language models function as lossy compression systems that learn by retaining only training data information relevant to their objectives, with research showing they approach optimal compression bounds for next-sequence prediction. Different models compress information differently based on their training data and methods, but compression optimality directly predicts downstream performance across various benchmarks.
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