Summary
This Reddit discussion post suggests two critical insights into Large Language Models (LLMs): that they learn in an unexpected 'backwards' manner, and more importantly, that the scaling hypothesis is bounded. This implies there are fundamental limits to performance gains achieved solely by increasing model size and data, potentially shifting future AI research directions.
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[r/ML] [D] MYTHOS-INVERSION STRUCTURAL AUDIT
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