Quantifying Hyperparameter Transfer and the Importance of Embedding Layer Learning Rate | Paper Citation Record

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This paper cites arXiv preprint arXiv:2601.10684 , year =.

Quantifying Hyperparameter Transfer and the Importance of Embedding Layer Learning Rate arXiv preprint arXiv:2601.10684 , year =

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This paper cites Power lines: Scaling laws for weight decay and batch size in LLM pre-training.

Quantifying Hyperparameter Transfer and the Importance of Embedding Layer Learning Rate Power lines: Scaling laws for weight decay and batch size in LLM pre-training

Reference 2

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This paper cites Scaling optimal LR across token horizons.

Quantifying Hyperparameter Transfer and the Importance of Embedding Layer Learning Rate Scaling optimal LR across token horizons

Reference 3

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This paper cites Self-consistent dynamical field theory of kernel evolution in wide neural networks.

Quantifying Hyperparameter Transfer and the Importance of Embedding Layer Learning Rate Self-consistent dynamical field theory of kernel evolution in wide neural networks

Reference 4

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This paper cites Infinite limits of multi-head transformer dynamics.

Quantifying Hyperparameter Transfer and the Importance of Embedding Layer Learning Rate Infinite limits of multi-head transformer dynamics

Reference 5

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This paper cites Depthwise hyperparam- eter transfer in residual networks: Dynamics and scaling limit.

Quantifying Hyperparameter Transfer and the Importance of Embedding Layer Learning Rate Depthwise hyperparam- eter transfer in residual networks: Dynamics and scaling limit

Reference 6

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This paper cites DeepSeek LLM: Scaling Open-Source Language Models with Longtermism.

Quantifying Hyperparameter Transfer and the Importance of Embedding Layer Learning Rate DeepSeek LLM: Scaling Open-Source Language Models with Longtermism

Reference 7

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This paper cites Don’t be lazy: Completep enables compute-efficient deep transformers.

Quantifying Hyperparameter Transfer and the Importance of Embedding Layer Learning Rate Don’t be lazy: Completep enables compute-efficient deep transformers

Reference 8

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This paper cites Don’t be lazy: Completep enables compute-efficient deep transformers.

Quantifying Hyperparameter Transfer and the Importance of Embedding Layer Learning Rate Don’t be lazy: Completep enables compute-efficient deep transformers

Reference 9

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This paper cites Sparse maximal update parameterization: A holistic approach to sparse training dynamics.

Quantifying Hyperparameter Transfer and the Importance of Embedding Layer Learning Rate Sparse maximal update parameterization: A holistic approach to sparse training dynamics

Reference 10

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This paper cites Scaling exponents across parameterizations and optimizers.

Quantifying Hyperparameter Transfer and the Importance of Embedding Layer Learning Rate Scaling exponents across parameterizations and optimizers

Reference 11

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This paper cites Understanding the mechanisms of fast hyperpa- rameter transfer.

Quantifying Hyperparameter Transfer and the Importance of Embedding Layer Learning Rate Understanding the mechanisms of fast hyperpa- rameter transfer

Reference 12

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This paper cites A loss curvature perspective on training instabilities of deep learning models.

Quantifying Hyperparameter Transfer and the Importance of Embedding Layer Learning Rate A loss curvature perspective on training instabilities of deep learning models

Reference 13

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This paper cites $\boldsymbol{\mu}\mathbf{P^2}$: Effective sharpness aware minimization requires layerwise perturbation scaling.

Quantifying Hyperparameter Transfer and the Importance of Embedding Layer Learning Rate $\boldsymbol{\mu}\mathbf{P^2}$: Effective sharpness aware minimization requires layerwise perturbation scaling

Reference 14

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This paper cites A proof of learning rate transfer under $\mu$p.

Quantifying Hyperparameter Transfer and the Importance of Embedding Layer Learning Rate A proof of learning rate transfer under $\mu$p

Reference 15

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This paper cites Optimal embedding learning rate in llms: The effect of vocabulary size.

Quantifying Hyperparameter Transfer and the Importance of Embedding Layer Learning Rate Optimal embedding learning rate in llms: The effect of vocabulary size

Reference 16

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Observation b12fee4f-c0fe-4629-9cfd-5c377a29cff1 · outbound

This paper cites Optimal Embedding Learning Rate in LLMs: The Effect of Vocabulary Size.

Quantifying Hyperparameter Transfer and the Importance of Embedding Layer Learning Rate Optimal Embedding Learning Rate in LLMs: The Effect of Vocabulary Size

Reference 17

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This paper cites Learning to grok: Emergence of in-context learning and skill composition in modular arithmetic tasks.

Quantifying Hyperparameter Transfer and the Importance of Embedding Layer Learning Rate Learning to grok: Emergence of in-context learning and skill composition in modular arithmetic tasks

Reference 18

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This paper cites An empirical analysis of compute-optimal large language model training.

Quantifying Hyperparameter Transfer and the Importance of Embedding Layer Learning Rate An empirical analysis of compute-optimal large language model training

Reference 19

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This paper cites MiniCPM: Unveiling the potential of small language models with scalable training strategies.

Quantifying Hyperparameter Transfer and the Importance of Embedding Layer Learning Rate MiniCPM: Unveiling the potential of small language models with scalable training strategies

Reference 20

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This paper cites Hyperparameter Transfer with Mixture-of-Expert Layers.

Quantifying Hyperparameter Transfer and the Importance of Embedding Layer Learning Rate Hyperparameter Transfer with Mixture-of-Expert Layers

Reference 21

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This paper cites Muon: An optimizer for hidden layers in neural networks.

Quantifying Hyperparameter Transfer and the Importance of Embedding Layer Learning Rate Muon: An optimizer for hidden layers in neural networks

Reference 22

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This paper cites Why warmup the learning rate? underlying mechanisms and improvements.

Quantifying Hyperparameter Transfer and the Importance of Embedding Layer Learning Rate Why warmup the learning rate? underlying mechanisms and improvements

Reference 23

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This paper cites Universal sharpness dynamics in neural network training: Fixed point analysis, edge of stability, and route to chaos.

Quantifying Hyperparameter Transfer and the Importance of Embedding Layer Learning Rate Universal sharpness dynamics in neural network training: Fixed point analysis, edge of stability, and route to chaos

Reference 24

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This paper cites Scaling Laws for Neural Language Models.

Quantifying Hyperparameter Transfer and the Importance of Embedding Layer Learning Rate Scaling Laws for Neural Language Models

Reference 25

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This paper cites Symmetry in language statistics shapes the geometry of model representations.

Quantifying Hyperparameter Transfer and the Importance of Embedding Layer Learning Rate Symmetry in language statistics shapes the geometry of model representations

Reference 26

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This paper cites nanoGPT: The simplest, fastest repository for training/finetuning medium-sized gpts.

Quantifying Hyperparameter Transfer and the Importance of Embedding Layer Learning Rate nanoGPT: The simplest, fastest repository for training/finetuning medium-sized gpts

Reference 27

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This paper cites Weight decay may matter more than µp for learning rate transfer in practice.

Quantifying Hyperparameter Transfer and the Importance of Embedding Layer Learning Rate Weight decay may matter more than µp for learning rate transfer in practice

Reference 28

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This paper cites Cifar-100 (canadian institute for advanced research).

Quantifying Hyperparameter Transfer and the Importance of Embedding Layer Learning Rate Cifar-100 (canadian institute for advanced research)

Reference 29

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This paper cites Predictable Scale: Part I, Step Law -- Optimal Hyperparameter Scaling Law in Large Language Model Pretraining.

Quantifying Hyperparameter Transfer and the Importance of Embedding Layer Learning Rate Predictable Scale: Part I, Step Law -- Optimal Hyperparameter Scaling Law in Large Language Model Pretraining

Reference 30

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Quantifying Hyperparameter Transfer and the Importance of Embedding Layer Learning Rate Adaptive optimization in the $\infty$-width limit

Reference 31

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Quantifying Hyperparameter Transfer and the Importance of Embedding Layer Learning Rate The Llama 3 Herd of Models

Reference 32

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Quantifying Hyperparameter Transfer and the Importance of Embedding Layer Learning Rate Decoupled weight decay regularization

Reference 33

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This paper cites µ-parametrization for mixture of experts.

Quantifying Hyperparameter Transfer and the Importance of Embedding Layer Learning Rate µ-parametrization for mixture of experts

Reference 34

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This paper cites Progress measures for grokking via mechanistic interpretability.

Quantifying Hyperparameter Transfer and the Importance of Embedding Layer Learning Rate Progress measures for grokking via mechanistic interpretability

Reference 35

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This paper cites Super consistency of neural network landscapes and learning rate transfer.

Quantifying Hyperparameter Transfer and the Importance of Embedding Layer Learning Rate Super consistency of neural network landscapes and learning rate transfer

Reference 36

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Quantifying Hyperparameter Transfer and the Importance of Embedding Layer Learning Rate 2 OLMo 2 Furious

Reference 37

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This paper cites The fineweb datasets: Decanting the web for the finest text data at scale.

Quantifying Hyperparameter Transfer and the Importance of Embedding Layer Learning Rate The fineweb datasets: Decanting the web for the finest text data at scale

Reference 38

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This paper cites Resolving discrepan- cies in compute-optimal scaling of language models.

Quantifying Hyperparameter Transfer and the Importance of Embedding Layer Learning Rate Resolving discrepan- cies in compute-optimal scaling of language models

Reference 39

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This paper cites Hyperparameter transfer enables consistent gains of matrix-preconditioned optimizers across scales.

Quantifying Hyperparameter Transfer and the Importance of Embedding Layer Learning Rate Hyperparameter transfer enables consistent gains of matrix-preconditioned optimizers across scales

Reference 40

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Quantifying Hyperparameter Transfer and the Importance of Embedding Layer Learning Rate Qwen2.5 Technical Report

Reference 41

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This paper cites Language models are unsupervised multitask learners.

Quantifying Hyperparameter Transfer and the Importance of Embedding Layer Learning Rate Language models are unsupervised multitask learners

Reference 42

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This paper cites Roberts, Sho Yaida, and Boris Hanin.Frontmatter, page i–iv.

Quantifying Hyperparameter Transfer and the Importance of Embedding Layer Learning Rate Roberts, Sho Yaida, and Boris Hanin.Frontmatter, page i–iv

Reference 43

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This paper cites On the infinite width limit of neural networks with a standard parameterization.

Quantifying Hyperparameter Transfer and the Importance of Embedding Layer Learning Rate On the infinite width limit of neural networks with a standard parameterization

Reference 44

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This paper cites (how) can transformers predict pseudo-random numbers? InForty-second International Conference on Machine Learning.

Quantifying Hyperparameter Transfer and the Importance of Embedding Layer Learning Rate (how) can transformers predict pseudo-random numbers? InForty-second International Conference on Machine Learning

Reference 45

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Quantifying Hyperparameter Transfer and the Importance of Embedding Layer Learning Rate On feature learning in structured state space models

Reference 46

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Quantifying Hyperparameter Transfer and the Importance of Embedding Layer Learning Rate Unresolved cited work

Reference 47

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This paper cites Attention is all you need.

Quantifying Hyperparameter Transfer and the Importance of Embedding Layer Learning Rate Attention is all you need

Reference 49

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This paper cites Meta-Principled Family of Hyperparameter Scaling Strategies.

Quantifying Hyperparameter Transfer and the Importance of Embedding Layer Learning Rate Meta-Principled Family of Hyperparameter Scaling Strategies

Reference 50

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Quantifying Hyperparameter Transfer and the Importance of Embedding Layer Learning Rate Unresolved cited work

Reference 51

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Observation 9ab1a6ab-dfb7-43b5-801b-e8df0e228b85 · outbound

This paper cites Tuning large neural networks via zero-shot hyperparameter transfer.

Quantifying Hyperparameter Transfer and the Importance of Embedding Layer Learning Rate Tuning large neural networks via zero-shot hyperparameter transfer

Reference 52

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This paper cites Tensor programs VI: Feature learning in infinite depth neural networks.

Quantifying Hyperparameter Transfer and the Importance of Embedding Layer Learning Rate Tensor programs VI: Feature learning in infinite depth neural networks

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-05-21T05:03:58.623087Z

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