Paper Citation Record · LEDGER

Towards joint scaling laws with optimal batch size schedules

As of 7 August 2026, this Paper Citation Record lists 43 of 43 outbound references and 0 inbound Pith citation observations for arXiv:2607.27731.

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

pith.paper-citation-record.v1
2607.27731 v1

Coverage vector

measured 43 of 43 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-01T02:30:31.465967Z

measured 43 of 43 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

43 of 43 outbound references displayed

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verified fuzzy0
unresolved43
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External citation measurements

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Outbound references

Observation a4f09abd-868d-49c5-b591-d1476a528b42 · outbound

This paper cites Scaling laws for neural language models , year =.

Towards joint scaling laws with optimal batch size schedules Scaling laws for neural language models , year =

Reference 1

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Observation 1557667a-c0d7-454d-9969-b215cfe19d9e · outbound

This paper cites Training compute-optimal large language models , year =.

Towards joint scaling laws with optimal batch size schedules Training compute-optimal large language models , year =

Reference 2

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Observation 16ad93d4-4ceb-48e0-8db6-1481f09af103 · outbound

This paper cites Tensor Programs V: Tuning large neural networks via zero-shot hyperparameter transfer , volume =.

Towards joint scaling laws with optimal batch size schedules Tensor Programs V: Tuning large neural networks via zero-shot hyperparameter transfer , volume =

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-01T02:30:31.289890Z

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Observation 7f78136f-96a6-4165-b9d5-902085a387f3 · outbound

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

Towards joint scaling laws with optimal batch size schedules Predictable Scale: Part I--Optimal Hyperparameter Scaling Law in Large Language Model Pretraining , year =

Reference 4

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Observation 02711bcc-cc9a-4db2-848c-9876b7659430 · outbound

This paper cites Measuring the effects of data parallelism on neural network training , volume =.

Towards joint scaling laws with optimal batch size schedules Measuring the effects of data parallelism on neural network training , volume =

Reference 5

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Observation 0f978401-68b8-4dad-b4bb-e42deeae8ace · outbound

This paper cites Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour.

Towards joint scaling laws with optimal batch size schedules Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour

Reference 6

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Observation 2ec2b4de-42b7-4e19-bb58-8908ac2c2090 · outbound

This paper cites ZeRO: memory optimizations toward training trillion parameter models , year =.

Towards joint scaling laws with optimal batch size schedules ZeRO: memory optimizations toward training trillion parameter models , year =

Reference 7

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Observation e81279bd-aaf9-43f2-9d47-d4aa02df63f6 · outbound

This paper cites An empirical model of large-batch training , year =.

Towards joint scaling laws with optimal batch size schedules An empirical model of large-batch training , year =

Reference 8

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Observation ce7e8e4b-a476-4543-8f1d-6e025e534118 · outbound

This paper cites On the computational inefficiency of large batch sizes for stochastic gradient descent , year =.

Towards joint scaling laws with optimal batch size schedules On the computational inefficiency of large batch sizes for stochastic gradient descent , year =

Reference 9

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Observation bfd4e69b-1c49-4792-b303-ada9b31010ee · outbound

This paper cites On Large-Batch Training for Deep Learning: Generalization Gap and Sharp Minima , year =.

Towards joint scaling laws with optimal batch size schedules On Large-Batch Training for Deep Learning: Generalization Gap and Sharp Minima , year =

Reference 10

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Observation 3b21ab53-952a-4089-88f6-a1cbf3d59dd8 · outbound

This paper cites Minicpm: Unveiling the potential of small language models with scalable training strategies , year =.

Towards joint scaling laws with optimal batch size schedules Minicpm: Unveiling the potential of small language models with scalable training strategies , year =

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-01T02:30:31.327601Z

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Observation 28bf1628-29bd-44be-991a-293b06f740aa · outbound

This paper cites How Does Critical Batch Size Scale in Pre-training? , year =.

Towards joint scaling laws with optimal batch size schedules How Does Critical Batch Size Scale in Pre-training? , year =

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-01T02:30:31.331876Z

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Observation 5027ad07-0b20-4b03-9850-98b41caee495 · outbound

This paper cites Power Lines: Scaling laws for weight decay and batch size in.

Towards joint scaling laws with optimal batch size schedules Power Lines: Scaling laws for weight decay and batch size in

Reference 13

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Observation b459aa26-2cb8-405e-b3a6-ee677284489d · outbound

This paper cites Scaling Law for Language Models Training Considering Batch Size , year =.

Towards joint scaling laws with optimal batch size schedules Scaling Law for Language Models Training Considering Batch Size , year =

Reference 14

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Observation c0bd5a46-c7a6-4767-aeed-c5e8fcebf2f9 · outbound

This paper cites Deepseek llm: Scaling open-source language models with longtermism , year =.

Towards joint scaling laws with optimal batch size schedules Deepseek llm: Scaling open-source language models with longtermism , year =

Reference 15

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Observation a5290cee-4251-4e1a-b6a0-c996454cd005 · outbound

This paper cites Advances in neural information processing systems , title =.

Towards joint scaling laws with optimal batch size schedules Advances in neural information processing systems , title =

Reference 16

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Observation 03c341b4-3a1b-4d35-a50c-f5e7a8bbc436 · outbound

This paper cites Dahl and Justin Gilmer and Christopher J.

Towards joint scaling laws with optimal batch size schedules Dahl and Justin Gilmer and Christopher J

Reference 17

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Observation 6e057ee9-cc2d-4ecb-8836-14f19ff387e4 · outbound

This paper cites Optimal linear decay learning rate schedules and further refinements , year =.

Towards joint scaling laws with optimal batch size schedules Optimal linear decay learning rate schedules and further refinements , year =

Reference 18

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Observation 27433306-36ab-42e8-971c-bc48da2de2ac · outbound

This paper cites International Conference on Machine Learning , title =.

Towards joint scaling laws with optimal batch size schedules International Conference on Machine Learning , title =

Reference 19

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Observation 919ecf2d-645d-47ec-82f7-5dd445e7e0c5 · outbound

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

Towards joint scaling laws with optimal batch size schedules Tensor programs VI: Feature learning in infinite depth neural networks , volume =

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-01T02:30:31.365901Z

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Observation 97fcd261-93b6-48d0-a4db-35a55fc1ccb4 · outbound

This paper cites Don't be lazy: CompleteP enables compute-efficient deep transformers , volume =.

Towards joint scaling laws with optimal batch size schedules Don't be lazy: CompleteP enables compute-efficient deep transformers , volume =

Reference 21

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Observation a8d021f4-1f17-4576-b734-ae540928446c · outbound

This paper cites Scaling optimal lr across token horizons , volume =.

Towards joint scaling laws with optimal batch size schedules Scaling optimal lr across token horizons , volume =

Reference 22

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Observation dccb3f46-a57e-4db8-8f3d-2afd3e0399f8 · outbound

This paper cites Convex Dominance in Deep Learning I: A Scaling Law of Loss and Learning Rate , year =.

Towards joint scaling laws with optimal batch size schedules Convex Dominance in Deep Learning I: A Scaling Law of Loss and Learning Rate , year =

Reference 23

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Observation 82787b68-d1cb-48ef-9b4a-74333a08ad79 · outbound

This paper cites Smith and Pieter-Jan Kindermans and Quoc V.

Towards joint scaling laws with optimal batch size schedules Smith and Pieter-Jan Kindermans and Quoc V

Reference 24

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Observation e03d0bed-efd4-4646-bc84-eba3f8953d77 · outbound

This paper cites Coupling Adaptive Batch Sizes with Learning Rates , year =.

Towards joint scaling laws with optimal batch size schedules Coupling Adaptive Batch Sizes with Learning Rates , year =

Reference 25

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Observation b81864e0-f5c8-4d73-b751-70207baf4b17 · outbound

This paper cites Kakade , title =.

Towards joint scaling laws with optimal batch size schedules Kakade , title =

Reference 26

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Observation f379c436-59ef-445b-8c6f-8ee89872d911 · outbound

This paper cites Critical Batch Size Revisited: A Simple Empirical Approach to Large-Batch Language Model Training , year =.

Towards joint scaling laws with optimal batch size schedules Critical Batch Size Revisited: A Simple Empirical Approach to Large-Batch Language Model Training , year =

Reference 27

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Observation c3b2249f-6b64-421d-b6b8-a1f7e0065a54 · outbound

This paper cites and Vinyals, Oriol and Sifre, Laurent , booktitle =.

Towards joint scaling laws with optimal batch size schedules and Vinyals, Oriol and Sifre, Laurent , booktitle =

Reference 28

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Observation 11fd10c9-c282-4a9e-a5b6-84f6012a6b8e · outbound

This paper cites Scaling Collapse Reveals Universal Dynamics in Compute-Optimally Trained Neural Networks , year =.

Towards joint scaling laws with optimal batch size schedules Scaling Collapse Reveals Universal Dynamics in Compute-Optimally Trained Neural Networks , year =

Reference 29

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This paper cites Scaling with collapse: Efficient and predictable training of llm families , year =.

Towards joint scaling laws with optimal batch size schedules Scaling with collapse: Efficient and predictable training of llm families , year =

Reference 30

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Observation 4cba753f-caf8-49fd-8aca-30701a4a05e0 · outbound

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Towards joint scaling laws with optimal batch size schedules nanoVLM , year =

Reference 31

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Observation 008879d9-3e34-4a87-b98b-14a6f47d0244 · outbound

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Towards joint scaling laws with optimal batch size schedules Unresolved cited work

Reference 32

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Towards joint scaling laws with optimal batch size schedules 2025 , url =

Reference 33

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Towards joint scaling laws with optimal batch size schedules Unresolved cited work

Reference 34

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Towards joint scaling laws with optimal batch size schedules Towards understanding of orthogonalization in muon , year =

Reference 35

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Towards joint scaling laws with optimal batch size schedules Decoupled Weight Decay Regularization , year =

Reference 36

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Towards joint scaling laws with optimal batch size schedules The llama 3 herd of models , year =

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Towards joint scaling laws with optimal batch size schedules Qwen3 technical report , year =

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-01T02:30:31.443635Z

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Observation 055257f7-ffc9-4e2f-a6bf-455e34d9fb58 · outbound

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Towards joint scaling laws with optimal batch size schedules Unresolved cited work

Reference 39

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Towards joint scaling laws with optimal batch size schedules Using deepspeed and megatron to train megatron-turing nlg 530b, a large-scale generative language model , year =

Reference 40

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Observation 7140253e-12c9-435c-a601-0936fe04c85a · outbound

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Towards joint scaling laws with optimal batch size schedules Unresolved cited work

Reference 41

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Observation 8faf06b4-5eca-473a-abfe-225e839683f7 · outbound

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Towards joint scaling laws with optimal batch size schedules Unresolved cited work

Reference 42

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Towards joint scaling laws with optimal batch size schedules 2023 , url =

Reference 43

Resolution: unresolved; no resolver link, observed 2026-08-01T02:30:31.465967Z

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