Paper Citation Record · LEDGER

No More Adam: Learning Rate Scaling at Initialization is All You Need

As of 12 August 2026, this Paper Citation Record lists 62 of 62 outbound references and 9 inbound Pith citation observations for arXiv:2412.11768.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2412.11768 v2

Coverage vector

measured 62 of 62 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-11T14:41:00.476703Z

measured 71 of 71 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-12T06:34:41.77262+00:00

measured 9 of 9 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-11T13:28:29.191879Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: arxiv_reference, observed 2026-05-22T13:24:53.244110Z

Reference resolution

62 of 62 outbound references displayed

verified exact4
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 5a23ca41-e8d1-43a6-904f-ab03cf556daa · outbound

This paper cites write newline.

No More Adam: Learning Rate Scaling at Initialization is All You Need write newline

Reference 1

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Observation 0e7eee76-3c22-4850-865b-ab454744731a · outbound

This paper cites S., Mehrotra, A., Dudziak, ., and Lane, N.

No More Adam: Learning Rate Scaling at Initialization is All You Need S., Mehrotra, A., Dudziak, ., and Lane, N

Reference 2

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Observation 70034392-ec84-4bd3-8ccb-3e8ed37e489c · outbound

This paper cites and Lavie, A.

No More Adam: Learning Rate Scaling at Initialization is All You Need and Lavie, A

Reference 3

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Observation 1c9fc9a5-4336-495e-b260-8f8f443e1401 · outbound

This paper cites signSGD: Compressed Optimisation for Non-Convex Problems.

No More Adam: Learning Rate Scaling at Initialization is All You Need signSGD: Compressed Optimisation for Non-Convex Problems

Reference 4

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Observation cd9e5b54-53a7-4f03-aa07-f83b16207700 · outbound

This paper cites Better plain ViT baselines for ImageNet-1k.

No More Adam: Learning Rate Scaling at Initialization is All You Need Better plain ViT baselines for ImageNet-1k

Reference 5

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Observation a64e9856-5b81-4d10-8f54-e95aff095684 · outbound

This paper cites Big vision.

No More Adam: Learning Rate Scaling at Initialization is All You Need Big vision

Reference 6

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Observation 46a82b51-02cb-4b4f-94f8-923ba24ef230 · outbound

This paper cites How does topology influence gradient propagation and model performance of deep networks with DenseNet-type skip connections?.

No More Adam: Learning Rate Scaling at Initialization is All You Need How does topology influence gradient propagation and model performance of deep networks with DenseNet-type skip connections?

Reference 7

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Observation 4b4bca43-ca98-4031-8e9a-b268f7c34380 · outbound

This paper cites A downsampled variant of imagenet as an alternative to the cifar datasets, 2017.

No More Adam: Learning Rate Scaling at Initialization is All You Need A downsampled variant of imagenet as an alternative to the cifar datasets, 2017

Reference 8

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Observation fd50a9df-52b0-47bd-b89e-a5a480944992 · outbound

This paper cites Imagenet: A large-scale hierarchical image database.

No More Adam: Learning Rate Scaling at Initialization is All You Need Imagenet: A large-scale hierarchical image database

Reference 9

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Observation cfdb851c-d78f-4b8c-b253-2d17c3a35a72 · outbound

This paper cites and Zettlemoyer, L.

No More Adam: Learning Rate Scaling at Initialization is All You Need and Zettlemoyer, L

Reference 10

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Observation 4dd42e93-584e-4f38-a86c-57b19a161d3b · outbound

This paper cites 8-bit Optimizers via Block-wise Quantization.

No More Adam: Learning Rate Scaling at Initialization is All You Need 8-bit Optimizers via Block-wise Quantization

Reference 11

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Observation ffe0f9c9-9af5-445b-b222-b30bf7f31d8b · outbound

This paper cites LLM.int8(): 8-bit Matrix Multiplication for Transformers at Scale.

No More Adam: Learning Rate Scaling at Initialization is All You Need LLM.int8(): 8-bit Matrix Multiplication for Transformers at Scale

Reference 12

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Observation 26231635-8a70-4e14-8997-9362671b1f4c · outbound

This paper cites Automatic evaluation of machine translation quality using n-gram co-occurrence statistics.

No More Adam: Learning Rate Scaling at Initialization is All You Need Automatic evaluation of machine translation quality using n-gram co-occurrence statistics

Reference 13

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Observation a7a57760-50ab-4763-b067-9cd6a06d30fe · outbound

This paper cites NATS-Bench : Benchmarking nas algorithms for architecture topology and size.

No More Adam: Learning Rate Scaling at Initialization is All You Need NATS-Bench : Benchmarking nas algorithms for architecture topology and size

Reference 14

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Observation a1c369c1-e0a9-45c9-9339-b5872222103c · outbound

This paper cites An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale.

No More Adam: Learning Rate Scaling at Initialization is All You Need An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 15

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Observation 9018f376-683a-4227-b46a-15b243e5e34e · outbound

This paper cites Incorporating Nesterov Momentum into Adam.

No More Adam: Learning Rate Scaling at Initialization is All You Need Incorporating Nesterov Momentum into Adam

Reference 16

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Observation de17ac81-8889-4062-84f1-8627e2608bdf · outbound

This paper cites Adaptive subgradient methods for online learning and stochastic optimization.

No More Adam: Learning Rate Scaling at Initialization is All You Need Adaptive subgradient methods for online learning and stochastic optimization

Reference 17

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Observation 3c41f2f4-da72-4268-9b6a-6e812d49d4ac · outbound

This paper cites The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks.

No More Adam: Learning Rate Scaling at Initialization is All You Need The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks

Reference 18

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Observation eccf62c8-7213-4717-bd66-77cc462288cc · outbound

This paper cites Pruning Neural Networks at Initialization: Why are We Missing the Mark?.

No More Adam: Learning Rate Scaling at Initialization is All You Need Pruning Neural Networks at Initialization: Why are We Missing the Mark?

Reference 19

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Observation a2e9d33e-5ad5-4198-bebe-5de0ba7413cb · outbound

This paper cites Improving Robustness with Adaptive Weight Decay.

No More Adam: Learning Rate Scaling at Initialization is All You Need Improving Robustness with Adaptive Weight Decay

Reference 20

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Observation c4402df0-377b-47a1-aaea-96a0e24f1c46 · outbound

This paper cites Adaptive Gradient Methods at the Edge of Stability.

No More Adam: Learning Rate Scaling at Initialization is All You Need Adaptive Gradient Methods at the Edge of Stability

Reference 21

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Observation 7519f124-418b-4358-80de-dcdf73fcf5ca · outbound

This paper cites and Cohen, V.

No More Adam: Learning Rate Scaling at Initialization is All You Need and Cohen, V

Reference 22

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Observation c1cd2074-91dc-42fb-9869-baca4a7608e8 · outbound

This paper cites Generating Sequences With Recurrent Neural Networks.

No More Adam: Learning Rate Scaling at Initialization is All You Need Generating Sequences With Recurrent Neural Networks

Reference 23

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Observation 8da474ab-e724-4b5a-b9ce-b87174212ef6 · outbound

This paper cites Z., Shi, Y., Chen, Y., Fan, Z., Xiao, W., Zhao, R., Chang, S., Wu, W., et al.

No More Adam: Learning Rate Scaling at Initialization is All You Need Z., Shi, Y., Chen, Y., Fan, Z., Xiao, W., Zhao, R., Chang, S., Wu, W., et al

Reference 24

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Observation a7928531-dfb2-4d62-af9a-38473cacb35e · outbound

This paper cites Deep Residual Learning for Image Recognition.

No More Adam: Learning Rate Scaling at Initialization is All You Need Deep Residual Learning for Image Recognition

Reference 25

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Observation 4b65eb7d-ecf1-4c4d-85d0-3aaf66469f48 · outbound

This paper cites Delving deep into rectifiers: Surpassing human-level performance on imagenet classification.

No More Adam: Learning Rate Scaling at Initialization is All You Need Delving deep into rectifiers: Surpassing human-level performance on imagenet classification

Reference 26

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Observation 7099f1fe-2fcc-4188-9bb6-dbb3eb9291fc · outbound

This paper cites Neural networks for machine learning lecture 6a overview of mini-batch gradient descent.

No More Adam: Learning Rate Scaling at Initialization is All You Need Neural networks for machine learning lecture 6a overview of mini-batch gradient descent

Reference 27

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This paper cites Denoising diffusion probabilistic models.

No More Adam: Learning Rate Scaling at Initialization is All You Need Denoising diffusion probabilistic models

Reference 28

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Observation 9cb65f2f-e8df-4321-b96f-36838c1a07da · outbound

This paper cites LoRA: Low-Rank Adaptation of Large Language Models.

No More Adam: Learning Rate Scaling at Initialization is All You Need LoRA: Low-Rank Adaptation of Large Language Models

Reference 29

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This paper cites Scaling Laws for Neural Language Models.

No More Adam: Learning Rate Scaling at Initialization is All You Need Scaling Laws for Neural Language Models

Reference 30

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This paper cites Adam: A Method for Stochastic Optimization.

No More Adam: Learning Rate Scaling at Initialization is All You Need Adam: A Method for Stochastic Optimization

Reference 31

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No More Adam: Learning Rate Scaling at Initialization is All You Need and Hinton, G

Reference 32

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This paper cites On weight initialization in deep neural networks.

No More Adam: Learning Rate Scaling at Initialization is All You Need On weight initialization in deep neural networks

Reference 33

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This paper cites Noise Is Not the Main Factor Behind the Gap Between SGD and Adam on Transformers, but Sign Descent Might Be.

No More Adam: Learning Rate Scaling at Initialization is All You Need Noise Is Not the Main Factor Behind the Gap Between SGD and Adam on Transformers, but Sign Descent Might Be

Reference 34

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This paper cites SNIP: Single-shot Network Pruning based on Connection Sensitivity.

No More Adam: Learning Rate Scaling at Initialization is All You Need SNIP: Single-shot Network Pruning based on Connection Sensitivity

Reference 35

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This paper cites Balance is Essence: Accelerating Sparse Training via Adaptive Gradient Correction.

No More Adam: Learning Rate Scaling at Initialization is All You Need Balance is Essence: Accelerating Sparse Training via Adaptive Gradient Correction

Reference 36

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Observation ef551001-d278-4bbf-8acd-24eeb31204c7 · outbound

This paper cites Memory Efficient Optimizers with 4-bit States.

No More Adam: Learning Rate Scaling at Initialization is All You Need Memory Efficient Optimizers with 4-bit States

Reference 37

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This paper cites Zico: Zero-shot nas via inverse coefficient of variation on gradients.

No More Adam: Learning Rate Scaling at Initialization is All You Need Zico: Zero-shot nas via inverse coefficient of variation on gradients

Reference 38

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This paper cites Convergence of adam under relaxed assumptions.

No More Adam: Learning Rate Scaling at Initialization is All You Need Convergence of adam under relaxed assumptions

Reference 39

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No More Adam: Learning Rate Scaling at Initialization is All You Need Rouge: A package for automatic evaluation of summaries

Reference 40

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This paper cites On the Variance of the Adaptive Learning Rate and Beyond.

No More Adam: Learning Rate Scaling at Initialization is All You Need On the Variance of the Adaptive Learning Rate and Beyond

Reference 41

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No More Adam: Learning Rate Scaling at Initialization is All You Need On the variance of the adaptive learning rate and beyond, 2021

Reference 42

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No More Adam: Learning Rate Scaling at Initialization is All You Need and Hutter, F

Reference 43

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No More Adam: Learning Rate Scaling at Initialization is All You Need Prodigy: An Expeditiously Adaptive Parameter-Free Learner

Reference 44

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No More Adam: Learning Rate Scaling at Initialization is All You Need Unresolved cited work

Reference 45

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This paper cites The E2E Dataset: New Challenges For End-to-End Generation.

No More Adam: Learning Rate Scaling at Initialization is All You Need The E2E Dataset: New Challenges For End-to-End Generation

Reference 46

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No More Adam: Learning Rate Scaling at Initialization is All You Need Bleu: a method for automatic evaluation of machine translation

Reference 47

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No More Adam: Learning Rate Scaling at Initialization is All You Need Language models are unsupervised multitask learners

Reference 48

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No More Adam: Learning Rate Scaling at Initialization is All You Need High-Resolution Image Synthesis with Latent Diffusion Models

Reference 49

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No More Adam: Learning Rate Scaling at Initialization is All You Need and Stern, M

Reference 50

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This paper cites How to train your ViT? Data, Augmentation, and Regularization in Vision Transformers.

No More Adam: Learning Rate Scaling at Initialization is All You Need How to train your ViT? Data, Augmentation, and Regularization in Vision Transformers

Reference 51

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No More Adam: Learning Rate Scaling at Initialization is All You Need L., and Ganguli, S

Reference 52

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No More Adam: Learning Rate Scaling at Initialization is All You Need Gemini: A Family of Highly Capable Multimodal Models

Reference 53

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No More Adam: Learning Rate Scaling at Initialization is All You Need Attention Is All You Need

Reference 54

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No More Adam: Learning Rate Scaling at Initialization is All You Need Cider: Consensus-based image description evaluation

Reference 55

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This paper cites Adagrad stepsizes: Sharp convergence over nonconvex landscapes.

No More Adam: Learning Rate Scaling at Initialization is All You Need Adagrad stepsizes: Sharp convergence over nonconvex landscapes

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:41:01.246624Z

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Observation 4f03eb27-e772-4db4-8146-8b5a2f54ccbd · outbound

This paper cites Exploiting network compressibility and topology in zero-cost nas.

No More Adam: Learning Rate Scaling at Initialization is All You Need Exploiting network compressibility and topology in zero-cost nas

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:41:01.228816Z

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No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation f7776041-aaae-4300-ae83-0542ecef541a · outbound

This paper cites Early Convolutions Help Transformers See Better.

No More Adam: Learning Rate Scaling at Initialization is All You Need Early Convolutions Help Transformers See Better

Reference 58

Resolution: unresolved; no resolver link, observed 2026-08-11T14:41:00.454756Z

Source-reported events for the cited work

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Observation 07d978a1-bd6f-440f-ae8c-6cf9dc4cd46e · outbound

This paper cites ADADELTA: An Adaptive Learning Rate Method.

No More Adam: Learning Rate Scaling at Initialization is All You Need ADADELTA: An Adaptive Learning Rate Method

Reference 59

Resolution: unresolved; no resolver link, observed 2026-08-11T14:41:00.459733Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0ac07514-e2bd-4385-b6dd-19edac1129d8 · outbound

This paper cites Riemannian Preconditioned LoRA for Fine-Tuning Foundation Models.

No More Adam: Learning Rate Scaling at Initialization is All You Need Riemannian Preconditioned LoRA for Fine-Tuning Foundation Models

Reference 60

Resolution: unresolved; no resolver link, observed 2026-08-11T14:41:00.465623Z

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Unavailable: canonical work link unavailable.

Observation 96bbed0d-d504-4638-8602-38806799ab8d · outbound

This paper cites Why Transformers Need Adam: A Hessian Perspective.

No More Adam: Learning Rate Scaling at Initialization is All You Need Why Transformers Need Adam: A Hessian Perspective

Reference 61

Resolution: unresolved; no resolver link, observed 2026-08-11T14:41:00.472499Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a7856eda-43d6-47fa-8ab5-68f7db99463a · outbound

This paper cites Adam-mini: Use Fewer Learning Rates To Gain More.

No More Adam: Learning Rate Scaling at Initialization is All You Need Adam-mini: Use Fewer Learning Rates To Gain More

Reference 62

Resolution: unresolved; no resolver link, observed 2026-08-11T14:41:00.476703Z

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Unavailable: canonical work link unavailable.

Pith citing papers

Observation 42800392-4b12-4e39-aad4-2377e9ba907e · inbound

SWAN: SGD with Normalization and Whitening Enables Stateless LLM Training cites this paper.

SWAN: SGD with Normalization and Whitening Enables Stateless LLM Training No More Adam: Learning Rate Scaling at Initialization is All You Need

Reference 65

Resolution: unresolved; no resolver link, observed 2026-08-11T13:28:29.191879Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 16e15bbc-102e-42d2-ac99-0f888910f114 · inbound

The Surprising Agreement Between Convex Optimization Theory and Learning-Rate Scheduling for Large Model Training cites this paper.

The Surprising Agreement Between Convex Optimization Theory and Learning-Rate Scheduling for Large Model Training No More Adam: Learning Rate Scaling at Initialization is All You Need

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-09T21:56:50.802232Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f4028448-fec2-4f51-9294-9d575a3c015a · inbound

Gradient Multi-Normalization for Stateless and Scalable LLM Training cites this paper.

Gradient Multi-Normalization for Stateless and Scalable LLM Training No More Adam: Learning Rate Scaling at Initialization is All You Need

Reference 44

Resolution: unresolved; no resolver link, observed 2026-08-08T14:37:23.786570Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 938f875f-cad8-42ce-916b-ecc6ec5e8f30 · inbound

Towards Efficient Optimizer Design for LLM via Structured Fisher Approximation with a Low-Rank Extension cites this paper.

Towards Efficient Optimizer Design for LLM via Structured Fisher Approximation with a Low-Rank Extension No More Adam: Learning Rate Scaling at Initialization is All You Need

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-08T11:46:47.782800Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c732579d-e615-4f62-8939-4f4a3b6dc7df · inbound

Memory-Efficient LLM Pretraining via Minimalist Optimizer Design cites this paper.

Memory-Efficient LLM Pretraining via Minimalist Optimizer Design No More Adam: Learning Rate Scaling at Initialization is All You Need

Reference 15

Resolution: verified exact; arxiv_id, observed 2026-05-22T13:24:53.247110Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 46d1497f-dc9c-4ad4-9a10-66c3666877b6 · inbound

Evolution of Optimization Methods: Algorithms, Scenarios, and Evaluations cites this paper.

Evolution of Optimization Methods: Algorithms, Scenarios, and Evaluations No More Adam: Learning Rate Scaling at Initialization is All You Need

Reference 32

Resolution: metadata mismatch; arxiv_id, observed 2026-05-11T10:51:20.166965Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation fffd8c75-6370-485e-bd02-c6bfaf3be839 · inbound

Layerwise LQR for Geometry-Aware Optimization of Deep Networks cites this paper.

Layerwise LQR for Geometry-Aware Optimization of Deep Networks No More Adam: Learning Rate Scaling at Initialization is All You Need

Reference 28

Resolution: verified exact; arxiv_id, observed 2026-05-11T17:51:09.813669Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 90d18fcd-5287-4f14-b605-6fccffadd7e3 · inbound

Revealing Modular Gradient Noise Imbalance in LLMs: Calibrating Adam via Signal-to-Noise Ratio cites this paper.

Revealing Modular Gradient Noise Imbalance in LLMs: Calibrating Adam via Signal-to-Noise Ratio No More Adam: Learning Rate Scaling at Initialization is All You Need

Reference 39

Resolution: metadata mismatch; arxiv_id, observed 2026-05-11T16:41:05.035119Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 2da8cb7e-00cc-4a91-b77c-518ee26566e6 · inbound

OmniOpt: Taxonomy, Geometry, and Benchmarking of Modern Optimizers cites this paper.

OmniOpt: Taxonomy, Geometry, and Benchmarking of Modern Optimizers No More Adam: Learning Rate Scaling at Initialization is All You Need

Reference 118

Resolution: unresolved; no resolver link, observed 2026-07-11T22:10:49.683444Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.