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Research questionHow do architecture and optimization shape accessible representations and scaling at finite budgets on the same data?Models trained on identical data can follow different loss-versus-budget curves because architecture and optimization may make different task-relevant representations accessible. Existing scaling descriptions do not fully explain how task geometry, architectural support, and finite-budget acquisition combine to determine those curves.
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Latest papersRecent research connected to this question, newest first.Coupled Scaling: A Representational Accessibility Framework for Neural Scaling LawsThe work presents a task-conditioned framework, a solvable mode-truncation analysis with residual and exponent bounds, and a fixed-kernel specialization. It motivates tests relating static and multiscale geometry to scaling behavior and audits released emergence trajectories, but the supplied evidence describes these as proposed tests and required controls rather than a completed direct factorial validation.research paper · Sep 3, 2026
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