Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-06-30T08:15:20.191222Z
Paper Citation Record · LEDGER
On the Nonlinearity of Learning Rate Scaling for LLM Training
As of 6 August 2026, this Paper Citation Record lists 48 of 48 outbound references and 0 inbound Pith citation observations for arXiv:2606.29158.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-05T06:32:48.257954+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links
A source-named dated measurement, never combined with another source.
Source: cited_works
Reference resolution
48 of 48 outbound references displayed
- verified exact14
- verified fuzzy26
- unresolved2
- parse uncertain0
- malformed identifier1
- metadata mismatch5
External citation measurements
No source-named external measurement is stored.
Outbound references
Observation 1ed80a9e-47b6-467a-a23c-9d2ae7b9bb60 · outbound
This paper cites Tune My Adam, Please!.
On the Nonlinearity of Learning Rate Scaling for LLM Training Tune My Adam, Please!
Reference 1
- Resolution
- verified exact
- arxiv_id, observed 2026-06-30T08:24:26.805017Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.
Observation b476bcbc-729f-4330-94de-9e18dd9d44db · outbound
This paper cites Layer Normalization.
On the Nonlinearity of Learning Rate Scaling for LLM Training Layer Normalization
Reference 2
- Resolution
- verified exact
- local_arxiv, observed 2026-06-30T08:24:26.802509Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.
Observation 2b49e810-a187-4c01-be95-494c36a627de · outbound
This paper cites Power lines: Scaling laws for weight decay and batch size in llm pre-training.
On the Nonlinearity of Learning Rate Scaling for LLM Training Power lines: Scaling laws for weight decay and batch size in llm pre-training
Reference 3
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-10T22:27:43.419325Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.
Observation d04d66e2-b1fe-4124-bc94-a89b80481c78 · outbound
This paper cites Scaling optimal lr across token horizons.
On the Nonlinearity of Learning Rate Scaling for LLM Training Scaling optimal lr across token horizons
Reference 4
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-10T22:27:43.453162Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.
Observation 2942bb31-e203-45f3-807e-d8b56bbdb2f0 · outbound
This paper cites Y., Deiseroth, B., Cruz-Salinas, A.
On the Nonlinearity of Learning Rate Scaling for LLM Training Y., Deiseroth, B., Cruz-Salinas, A
Reference 5
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-10T22:27:43.352095Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.
Observation 85bfd103-e0ed-4bf5-8edf-3ae048ad1712 · outbound
This paper cites Depthwise hyperparameter transfer in residual networks: Dynamics and scaling limit.
On the Nonlinearity of Learning Rate Scaling for LLM Training Depthwise hyperparameter transfer in residual networks: Dynamics and scaling limit
Reference 6
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-10T22:27:43.368176Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.
Observation 326afa66-a69d-41c6-b494-96c0f9d9240f · outbound
This paper cites DeepSeek LLM: Scaling Open-Source Language Models with Longtermism.
On the Nonlinearity of Learning Rate Scaling for LLM Training DeepSeek LLM: Scaling Open-Source Language Models with Longtermism
Reference 7
- Resolution
- metadata mismatch
- local_arxiv, observed 2026-06-30T08:24:26.799840Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.
Observation acdbe677-c97d-4750-a1ca-d887f791c183 · outbound
This paper cites Don’t be lazy: Completep enables compute-efficient deep transformers.
On the Nonlinearity of Learning Rate Scaling for LLM Training Don’t be lazy: Completep enables compute-efficient deep transformers
Reference 8
- Resolution
- metadata mismatch
- arxiv_id, observed 2026-06-30T08:24:26.796924Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.
Observation fad2912c-901a-4aa5-ac76-44710a4a7a07 · outbound
This paper cites E., Xiao, L., Wortsman, M., Alemi, A.
On the Nonlinearity of Learning Rate Scaling for LLM Training E., Xiao, L., Wortsman, M., Alemi, A
Reference 9
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-10T22:27:43.399756Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.
Observation 8cfee3b9-eef0-4808-9210-cc8a42fb94df · outbound
This paper cites Robust layerwise scaling rules by proper weight decay tuning.
On the Nonlinearity of Learning Rate Scaling for LLM Training Robust layerwise scaling rules by proper weight decay tuning
Reference 10
- Resolution
- verified exact
- arxiv_id, observed 2026-06-30T08:24:26.782528Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.
Observation d5ba71d2-ef7d-4300-a530-e599984dba5b · outbound
This paper cites Nemotron-flash: Towards latency-optimal hybrid small language models.
On the Nonlinearity of Learning Rate Scaling for LLM Training Nemotron-flash: Towards latency-optimal hybrid small language models
Reference 11
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-10T22:27:43.337043Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.
Observation 5e5b1cde-973b-4d68-9a8f-4bef60ff7942 · outbound
This paper cites Norm matters: efficient and accurate normalization schemes in deep networks.Advances in Neural Information Processing Systems, 31, 2018.
On the Nonlinearity of Learning Rate Scaling for LLM Training Norm matters: efficient and accurate normalization schemes in deep networks.Advances in Neural Information Processing Systems, 31, 2018
Reference 12
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-10T22:27:43.292267Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.
Observation b6e36f17-a54f-4e53-a522-bcbc3966accc · outbound
This paper cites Minicpm: Unveiling the potential of small language models with scalable training strategies.
On the Nonlinearity of Learning Rate Scaling for LLM Training Minicpm: Unveiling the potential of small language models with scalable training strategies
Reference 13
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-10T22:27:43.275959Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.
Observation b9fc8db1-5b25-45c6-b8e5-5912fbd4607b · outbound
This paper cites H., and Leyton-Brown, K.
On the Nonlinearity of Learning Rate Scaling for LLM Training H., and Leyton-Brown, K
Reference 14
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-10T22:27:43.307151Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.
Observation 216b8889-4b67-4bab-a95b-9f06bbd1caa0 · outbound
This paper cites and Szegedy, C.
On the Nonlinearity of Learning Rate Scaling for LLM Training and Szegedy, C
Reference 15
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-10T22:27:43.321669Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.
Observation f1ed5e63-8183-4b39-9fb3-943fbbad2508 · outbound
This paper cites Three Factors Influencing Minima in SGD.
On the Nonlinearity of Learning Rate Scaling for LLM Training Three Factors Influencing Minima in SGD
Reference 16
- Resolution
- verified exact
- local_arxiv, observed 2026-06-30T08:24:26.785633Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.
Observation b2d893dc-ba19-47b2-9599-720bda639dba · outbound
This paper cites Scaling Laws for Neural Language Models.
On the Nonlinearity of Learning Rate Scaling for LLM Training Scaling Laws for Neural Language Models
Reference 17
- Resolution
- metadata mismatch
- local_arxiv, observed 2026-06-30T08:24:26.788145Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.
Observation 93482c5a-9c43-43d1-8828-1011787364a5 · outbound
This paper cites nanoGPT.https://github.com/karpathy/nanoGPT, 2022.
On the Nonlinearity of Learning Rate Scaling for LLM Training nanoGPT.https://github.com/karpathy/nanoGPT, 2022
Reference 18
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-10T22:27:43.383752Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.
Observation 70994301-6fd4-488d-9b4b-cb94d4fd16c2 · outbound
This paper cites Rotational equilibrium: How weight decay balances learning across neural networks.
On the Nonlinearity of Learning Rate Scaling for LLM Training Rotational equilibrium: How weight decay balances learning across neural networks
Reference 19
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-10T22:27:43.437186Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.
Observation 1d47b165-16e1-4c2f-b048-95fcb2473e01 · outbound
This paper cites Weight decay may matter more than mup for learning rate transfer in practice.
On the Nonlinearity of Learning Rate Scaling for LLM Training Weight decay may matter more than mup for learning rate transfer in practice
Reference 20
- Resolution
- verified exact
- arxiv_id, observed 2026-06-30T08:24:26.791192Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.
Observation 9b8a732c-2eb5-45f3-af9d-0924c8286b4f · outbound
This paper cites Efficient hyperparameter tuning via trajectory invariance principle.arXiv preprint arXiv:2509.25049, 2025.
On the Nonlinearity of Learning Rate Scaling for LLM Training Efficient hyperparameter tuning via trajectory invariance principle.arXiv preprint arXiv:2509.25049, 2025
Reference 21
- Resolution
- verified exact
- arxiv_id, observed 2026-06-30T08:24:26.772306Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.
Observation 23542f01-bb32-401a-9b90-90b1e45102bb · outbound
This paper cites Predictable Scale: Part I, Step Law -- Optimal Hyperparameter Scaling Law in Large Language Model Pretraining.
On the Nonlinearity of Learning Rate Scaling for LLM Training Predictable Scale: Part I, Step Law -- Optimal Hyperparameter Scaling Law in Large Language Model Pretraining
Reference 22
- Resolution
- verified exact
- arxiv_id, observed 2026-06-30T08:24:26.775578Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.
Observation 910d5cd0-ae41-426c-805e-22c39419f4f0 · outbound
This paper cites Reconciling modern deep learning with traditional optimization analyses: The intrinsic learning rate.Advances in Neural Information Processing Systems, 33: 14544–14555, 2020.
On the Nonlinearity of Learning Rate Scaling for LLM Training Reconciling modern deep learning with traditional optimization analyses: The intrinsic learning rate.Advances in Neural Information Processing Systems, 33: 14544–14555, 2020
Reference 23
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-10T22:27:43.470216Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.
Observation fc444885-f740-4f4d-bc3f-7df9c1d70f0a · outbound
This paper cites Muon is Scalable for LLM Training.
On the Nonlinearity of Learning Rate Scaling for LLM Training Muon is Scalable for LLM Training
Reference 24
- Resolution
- verified exact
- local_arxiv, observed 2026-06-30T08:24:26.786545Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.
Observation d7dd65cb-4a23-4d67-83b9-227a7d2daab7 · outbound
This paper cites and Hutter, F.
On the Nonlinearity of Learning Rate Scaling for LLM Training and Hutter, F
Reference 25
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-10T22:27:43.177327Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.
Observation f04a8424-4645-408b-a2fd-edd22af09e6b · outbound
This paper cites ngpt: Normalized transformer with representation learning on the hypersphere.
On the Nonlinearity of Learning Rate Scaling for LLM Training ngpt: Normalized transformer with representation learning on the hypersphere
Reference 26
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-10T22:27:43.193385Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.
Observation fb782221-1cd1-4f28-9fa5-ed58f30af273 · outbound
This paper cites A multi-power law for loss curve prediction across learning rate schedules.
On the Nonlinearity of Learning Rate Scaling for LLM Training A multi-power law for loss curve prediction across learning rate schedules
Reference 27
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-10T22:27:43.209774Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.
Observation 7336a97c-5837-4d9d-832f-2fce3e899626 · outbound
This paper cites On the sdes and scaling rules for adaptive gradient algorithms.Advances in Neural Information Processing Systems, 35:7697–7711, 2022.
On the Nonlinearity of Learning Rate Scaling for LLM Training On the sdes and scaling rules for adaptive gradient algorithms.Advances in Neural Information Processing Systems, 35:7697–7711, 2022
Reference 28
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-10T22:27:43.241692Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.
Observation 70b8bbfd-6f20-459a-b87b-080c0bc99d32 · outbound
This paper cites G., and Goldblum, M.
On the Nonlinearity of Learning Rate Scaling for LLM Training G., and Goldblum, M
Reference 29
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-10T22:27:43.161987Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.
Observation 6c3812b3-5e75-4cf6-8c25-6dd533c7b0c2 · outbound
This paper cites An Empirical Model of Large-Batch Training.
On the Nonlinearity of Learning Rate Scaling for LLM Training An Empirical Model of Large-Batch Training
Reference 30
- Resolution
- verified exact
- local_arxiv, observed 2026-06-30T08:24:26.769946Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.
Observation 8d1dc6d3-028a-4b07-aa18-9ce07405935b · outbound
This paper cites Completed hyperparameter transfer across modules, width, depth, batch and duration.
On the Nonlinearity of Learning Rate Scaling for LLM Training Completed hyperparameter transfer across modules, width, depth, batch and duration
Reference 31
- Resolution
- verified exact
- arxiv_id, observed 2026-06-30T08:24:26.772683Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.
Observation f0dc6fca-e4f4-49b3-be1a-e3b4f7e87669 · outbound
This paper cites The fineweb datasets: Decanting the web for the finest text data at scale.Advances in Neural Information Processing Systems, 37:30811–30849, 2024.
On the Nonlinearity of Learning Rate Scaling for LLM Training The fineweb datasets: Decanting the web for the finest text data at scale.Advances in Neural Information Processing Systems, 37:30811–30849, 2024
Reference 32
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-10T22:27:43.258185Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.
Observation 70f0f246-979c-4d3a-9506-8d9675bd2dab · outbound
This paper cites Resolving Discrepancies in Compute-Optimal Scaling of Language Models.
On the Nonlinearity of Learning Rate Scaling for LLM Training Resolving Discrepancies in Compute-Optimal Scaling of Language Models
Reference 33
- Resolution
- verified exact
- arxiv_id, observed 2026-06-30T08:24:26.779384Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.
Observation a7ae3b72-9161-42ae-9408-24e534c7285f · outbound
This paper cites Language models are un- supervised multitask learners.
On the Nonlinearity of Learning Rate Scaling for LLM Training Language models are un- supervised multitask learners
Reference 34
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-10T22:27:43.226047Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.
Observation 87995147-01b3-4849-ad29-b17493b54b62 · outbound
This paper cites an unresolved cited work.
On the Nonlinearity of Learning Rate Scaling for LLM Training Unresolved cited work
Reference 35
- Resolution
- unresolved
- raw_fallback, observed 2026-07-10T22:27:43.146959Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.
Observation 068cd734-0431-4fcc-8a9b-977367f48ca5 · outbound
This paper cites Scaling Law with Learning Rate Annealing.
On the Nonlinearity of Learning Rate Scaling for LLM Training Scaling Law with Learning Rate Annealing
Reference 36
- Resolution
- verified exact
- arxiv_id, observed 2026-06-30T08:24:26.793968Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.
Observation 1b0bb89b-2852-45b5-8e8c-2359a173e3d5 · outbound
This paper cites L2 Regularization versus Batch and Weight Normalization.
On the Nonlinearity of Learning Rate Scaling for LLM Training L2 Regularization versus Batch and Weight Normalization
Reference 37
- Resolution
- verified exact
- local_arxiv, observed 2026-06-30T08:24:26.807479Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.
Observation ec48e0a1-bd5e-4a9c-b52e-9034a6388842 · outbound
This paper cites N., Kaiser, L.
On the Nonlinearity of Learning Rate Scaling for LLM Training N., Kaiser, L
Reference 38
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-10T22:27:43.033764Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.
Observation 0b715f71-b032-424c-bf81-edaac5d03c5b · outbound
This paper cites The sharpness disparity principle in transformers for accelerating language model pre-training.
On the Nonlinearity of Learning Rate Scaling for LLM Training The sharpness disparity principle in transformers for accelerating language model pre-training
Reference 39
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-10T22:27:43.087074Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.
Observation 81f308be-1414-4f09-97d9-38b1fe3beed7 · outbound
This paper cites Scaling laws across model architectures: A comparative analysis of dense and M o E models in large language models.
On the Nonlinearity of Learning Rate Scaling for LLM Training Scaling laws across model architectures: A comparative analysis of dense and M o E models in large language models
Reference 40
- Resolution
- verified exact
- doi, observed 2026-06-30T08:24:26.153118Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.
Observation 14de7d54-e1a2-439f-a978-41d89baa9892 · outbound
This paper cites and Aitchison, L.
On the Nonlinearity of Learning Rate Scaling for LLM Training and Aitchison, L
Reference 41
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-10T22:27:43.018928Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.
Observation d5b9adb0-4a9f-418d-bc2e-5ad7c3c40901 · outbound
This paper cites Fantastic pretraining optimizers and where to find them 2.1: Hyperball optimization, 12 2025.
On the Nonlinearity of Learning Rate Scaling for LLM Training Fantastic pretraining optimizers and where to find them 2.1: Hyperball optimization, 12 2025
Reference 42
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-10T22:27:43.117232Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.
Observation ed70c51b-7dd4-42f4-9e0f-e43c287b5ac2 · outbound
This paper cites Controlled llm training on spectral sphere.
On the Nonlinearity of Learning Rate Scaling for LLM Training Controlled llm training on spectral sphere
Reference 43
- Resolution
- metadata mismatch
- arxiv_id, observed 2026-06-30T08:24:26.775149Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.
Observation ac30bcd1-4fd1-4bdd-b1f5-2cddd2272537 · outbound
This paper cites and Hu, E.
On the Nonlinearity of Learning Rate Scaling for LLM Training and Hu, E
Reference 44
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-10T22:27:43.051503Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.
Observation 31917e82-803b-4c49-8f4a-7a98ccdc03d2 · outbound
This paper cites Tuning large neural networks via zero-shot hyperparameter transfer.Advances in Neural Information Processing Systems, 34:17084–17097, 2021.
On the Nonlinearity of Learning Rate Scaling for LLM Training Tuning large neural networks via zero-shot hyperparameter transfer.Advances in Neural Information Processing Systems, 34:17084–17097, 2021
Reference 45
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-10T22:27:43.069067Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.
Observation 46be5cf6-44e2-4b78-b138-60d16ba37af6 · outbound
This paper cites an unresolved cited work.
On the Nonlinearity of Learning Rate Scaling for LLM Training Unresolved cited work
Reference 46
- Resolution
- unresolved
- raw_fallback, observed 2026-07-10T22:27:43.132477Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.
Observation fbcbe078-632d-4549-a4b2-31dae2eaa7de · outbound
This paper cites arXiv , author =:2602.10300 , file =.
On the Nonlinearity of Learning Rate Scaling for LLM Training arXiv , author =:2602.10300 , file =
Reference 47
- Resolution
- metadata mismatch
- arxiv_id, observed 2026-06-30T08:24:26.784084Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.
Observation 1e8e661a-dcd3-42cf-8e8d-9ce6fdbf9989 · outbound
This paper cites How to set the learning rate for large-scale pre-training?arXiv preprint arXiv:2601.05049, 2026.
On the Nonlinearity of Learning Rate Scaling for LLM Training How to set the learning rate for large-scale pre-training?arXiv preprint arXiv:2601.05049, 2026
Reference 48
- Resolution
- malformed identifier
- arxiv_id, observed 2026-06-30T08:24:26.758109Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.
Pith citing papers
No inbound Pith citation observations are available.