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ARXIV:2603.15958 · OPTIMIZATION THEORY · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE
ARXIV:2603.15958OPTIMIZATION THEORYSUBMITTED 02 APR · 02:30 UTCFRESHNESS STALEarXiv
This paper explores hyperparameter scaling laws for optimizers through modern optimization theory.
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Pain This paper explores hyperparameter scaling laws for optimizers through modern optimization theory.
Evidence 0 refs | 0 sources | 17% coverage
Blocker Evidence unverified
This paper explores hyperparameter scaling laws for optimizers through modern optimization theory. Existing methods, such as muP, primarily focus on transfer between model sizes, with transfer across batch sizes and training horizons often relying…
Hyperparameter transfer has become an important component of modern large-scale training recipes. Existing methods, such as muP, primarily focus on transfer between model sizes, with transfer across batch sizes and training horizons often relying…
ScienceToStartup currently rates this 2.0/10 on the public viability pass. Our results draw particular attention to the interaction between momentum and batch-size scaling, suggesting that optimal performance may be achieved with several scaling strategies.
Optimization Theory moved forward this cycle; last verified April 2026. Public score 2.0/10.
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This paper explores hyperparameter scaling laws for optimizers through modern optimization theory.
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10.48550/arXiv.2603.15958This paper explores hyperparameter scaling laws for optimizers through modern optimization theory.
Abstract
Hyperparameter transfer has become an important component of modern large-scale training recipes. Existing methods, such as muP, primarily focus on transfer between model sizes, with transfer across batch sizes and training horizons often relying on empirical scaling rules informed by insights from timescale preservation, quadratic proxies, and continuous-time approximations. We study hyperparameter scaling laws for modern first-order optimizers through the lens of recent convergence bounds for methods based on the Linear Minimization Oracle (LMO), a framework that includes normalized SGD, signSGD (approximating Adam), and Muon. Treating bounds in recent literature as a proxy and minimizing them across different tuning regimes yields closed-form power-law schedules for learning rate, momentum, and batch size as functions of the iteration or token budget. Our analysis, holding model size fixed, recovers most insights and observations from the literature under a unified and principled perspective, with clear directions open for future research. Our results draw particular attention to the interaction between momentum and batch-size scaling, suggesting that optimal performance may be achieved with several scaling strategies.
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PROBLEM
This paper explores hyperparameter scaling laws for optimizers through modern optimization theory. Existing methods, such as muP, primarily focus on transfer between model sizes, with transfer across batch sizes and training horizons often relying on empirical scaling rules info...
METHOD
Hyperparameter transfer has become an important component of modern large-scale training recipes. Existing methods, such as muP, primarily focus on transfer between model sizes, with transfer across batch sizes and training horizons often relying on empirical scaling rules infor...
RESULT
ScienceToStartup currently rates this 2.0/10 on the public viability pass. Our results draw particular attention to the interaction between momentum and batch-size scaling, suggesting that optimal performance may be achieved with several scaling strategies.
WHY NOW
Optimization Theory moved forward this cycle; last verified April 2026. Public score 2.0/10.
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This paper explores hyperparameter scaling laws for optimizers through modern optimization theory. Existing methods, such as muP, primarily focus on transfer between model sizes, with transfer across batch sizes and training horizons often relying on empirical scaling rules informed by insights from timescale preservation, quadratic proxies, and continuous-time approximations.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Hyperparameter transfer has become an important component of modern large-scale training recipes. Existing methods, such as muP, primarily focus on transfer between model sizes, with transfer across batch sizes and training horizons often relying on empirical scaling rules informed by insights from timescale preservation, quadratic proxies, and continuous-time approximations.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 2.0/10 on the public viability pass. Our results draw particular attention to the interaction between momentum and batch-size scaling, suggesting that optimal performance may be achieved with several scaling strategies.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Optimization Theory moved forward this cycle; last verified April 2026. Public score 2.0/10.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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This paper explores hyperparameter scaling laws for optimizers through modern optimization theory.
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Optimization Theory
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