LLM scaling exponents near 0.1 imply that reducing loss tenfold would need 10^10 more compute, making scientific-grade reliability unreachable by brute-force scaling.
A note on the physical interpretation of neural PDE's
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abstract
We highlight a formal and substantial analogy between Machine Learning (ML) algorithms and discrete dynamical systems (DDS) in relaxation form. The analogy offers a transparent interpretation of the weights in terms of physical information-propagation processes and identifies the model function of the forward ML step with the local attractor of the corresponding discrete dynamics. Besides improving the explainability of current ML applications, this analogy may also facilitate the development of a new class ML algorithms with a reduced number of weights.
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The wall confronting large language models
LLM scaling exponents near 0.1 imply that reducing loss tenfold would need 10^10 more compute, making scientific-grade reliability unreachable by brute-force scaling.