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papersTODAY 04:00 UTC

Paper Proposes Representational Accessibility Framework for Neural Scaling Laws

A revised arXiv preprint introduces "coupled scaling," a framework that explains when two learning systems trained on the same task under identical resource constraints should exhibit matching scaling rates and when they should diverge. The approach ties scaling behavior to representational accessibility, describing how easily a model can reach task-relevant features.

arXivcoupled-scalingneural-scaling-lawsrepresentational-accessibility

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