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Paper Citation Record · LEDGER

WSqD: A Horizon-Free Learning Rate Schedule for Large Model Training

As of 15 August 2026, this Paper Citation Record lists 61 of 61 outbound references and 0 inbound Pith citation observations for arXiv:2607.10959.

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

pith.paper-citation-record.v1
2607.10959 v1

Coverage vector

measured 61 of 61 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-07-14T08:04:06.432613Z

measured 61 of 61 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-15T06:32:42.880941+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

61 of 61 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved61
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 4b968111-fd5f-4ffd-b182-d6ac105ee651 · outbound

This paper cites Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , pages =.

WSqD: A Horizon-Free Learning Rate Schedule for Large Model Training Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , pages =

Reference 1

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source=arxiv_source observed=2026-07-14T08:04:06.432613Z digest=sha256:329b0080b708f734ad0d9be7a4a8a6da317d16e675b77dfdbe323f965ce436f0

Observation 623937b3-3ef9-447c-9f91-63d10c3b37ff · outbound

This paper cites 2017 , publisher=.

WSqD: A Horizon-Free Learning Rate Schedule for Large Model Training 2017 , publisher=

Reference 2

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source=arxiv_source observed=2026-07-14T08:04:06.432613Z digest=sha256:a2d2f1a9639ec4618599765a36c6706654a157e816870d00c07db659b5b2b0ce

Observation 7cb5aa6c-7e3c-4a66-83d3-f64e28497e78 · outbound

This paper cites an unresolved cited work.

WSqD: A Horizon-Free Learning Rate Schedule for Large Model Training Unresolved cited work

Reference 3

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source=arxiv_source observed=2026-07-14T08:04:06.432613Z digest=sha256:ea5ee9f0f3e3fd4c9dc10fa9a001a4560c3c4dbe752251c7e6574438cb7e6b8d

Observation 08b9c5ff-8cd8-4f80-80f7-5ba8e18e9cf7 · outbound

This paper cites an unresolved cited work.

WSqD: A Horizon-Free Learning Rate Schedule for Large Model Training Unresolved cited work

Reference 4

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source=arxiv_source observed=2026-07-14T08:04:06.432613Z digest=sha256:d3377a9f689d604b4e18565ba03e6fa59b7a34225b621f5e4d8b212252f61317

Observation 8f4e4d89-7fa2-4a8b-a650-ced27cc70e38 · outbound

This paper cites an unresolved cited work.

WSqD: A Horizon-Free Learning Rate Schedule for Large Model Training Unresolved cited work

Reference 5

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source=arxiv_source observed=2026-07-14T08:04:06.432613Z digest=sha256:5c7801f9e0abe0efbb14fcd6b68c0776d63c9161da863401db9a5978d60478cd

Observation 58d410af-f73d-4cc6-a8c9-3971984c4c02 · outbound

This paper cites an unresolved cited work.

WSqD: A Horizon-Free Learning Rate Schedule for Large Model Training Unresolved cited work

Reference 6

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source=arxiv_source observed=2026-07-14T08:04:06.432613Z digest=sha256:7a688550b2b3e32c1dc2c9a9f56d9456eac3a218d9df0e9c4cf0bfc8b248b5d8

Observation e8122511-0a4b-40c7-b8de-eca97b870ba0 · outbound

This paper cites Advances in Neural Information Processing Systems , year =.

WSqD: A Horizon-Free Learning Rate Schedule for Large Model Training Advances in Neural Information Processing Systems , year =

Reference 7

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source=arxiv_source observed=2026-07-14T08:04:06.432613Z digest=sha256:d5373c26fbbd0dc2007622a66449e835a8598e897b3b88b946892f30d6acc5cd

Observation 60eb9750-6b4d-457b-8c2b-930d7bb930a8 · outbound

This paper cites Optimal Linear Decay Learning Rate Schedules and Further Refinements.

WSqD: A Horizon-Free Learning Rate Schedule for Large Model Training Optimal Linear Decay Learning Rate Schedules and Further Refinements

Reference 8

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source=arxiv_source observed=2026-07-14T08:04:06.432613Z digest=sha256:d1929ed1d4995ed65cf94458b52778587c5d54b399771df555ffb60db871ac07

Observation 3ebd8729-cbf8-45b8-bbc3-1b34afc21744 · outbound

This paper cites Advances in Neural Information Processing Systems , volume=.

WSqD: A Horizon-Free Learning Rate Schedule for Large Model Training Advances in Neural Information Processing Systems , volume=

Reference 9

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Observation d014b6bf-1a40-4b49-b7f5-7a7498118a2b · outbound

This paper cites an unresolved cited work.

WSqD: A Horizon-Free Learning Rate Schedule for Large Model Training Unresolved cited work

Reference 10

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source=arxiv_source observed=2026-07-14T08:04:06.432613Z digest=sha256:3d228ef666d0401e570f72e6ab58749f8acac26fbd17db6c5f88c34a4433b2dd

Observation a33951fc-dcc2-4d0d-8ac5-3b41959ae176 · outbound

This paper cites International Conference on Learning Representations , year =.

WSqD: A Horizon-Free Learning Rate Schedule for Large Model Training International Conference on Learning Representations , year =

Reference 11

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Observation 708e1389-ec1e-47d4-acc4-1a029c23e117 · outbound

This paper cites Simple and Scalable Strategies to Continually Pre-train Large Language Models.

WSqD: A Horizon-Free Learning Rate Schedule for Large Model Training Simple and Scalable Strategies to Continually Pre-train Large Language Models

Reference 12

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source=arxiv_source observed=2026-07-14T08:04:06.432613Z digest=sha256:cd38fda48c670ef035d50f089a601c00fdcaebf945b55801597128c9cd136393

Observation 94ef733a-0294-4260-bb27-a08c404f13d1 · outbound

This paper cites Advances in Neural Information Processing Systems , volume =.

WSqD: A Horizon-Free Learning Rate Schedule for Large Model Training Advances in Neural Information Processing Systems , volume =

Reference 13

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source=arxiv_source observed=2026-07-14T08:04:06.432613Z digest=sha256:d7d6b4ba64df84ca7ea2c17d7e616063aadcda8e6111cfcc736932f0d3687f90

Observation f522eb20-3147-4bf7-9b0f-b92fa207bf6a · outbound

This paper cites an unresolved cited work.

WSqD: A Horizon-Free Learning Rate Schedule for Large Model Training Unresolved cited work

Reference 14

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source=arxiv_source observed=2026-07-14T08:04:06.432613Z digest=sha256:6b0d8d55b9f6287e354a23355895a3c278fc9673ee8c6af2fc5099f8fefed32e

Observation 8a18bf9e-8f8f-4c0c-95c9-cba372d83ae5 · outbound

This paper cites DeepSeek-V3 Technical Report.

WSqD: A Horizon-Free Learning Rate Schedule for Large Model Training DeepSeek-V3 Technical Report

Reference 15

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source=arxiv_source observed=2026-07-14T08:04:06.432613Z digest=sha256:d48aacc8bea1e1ae29aaf69ed6ca3768bf6b76845a81816e941d5d06dac3277f

Observation 14da3bf0-4d5a-4635-9db6-74111616e6a9 · outbound

This paper cites Continual Pre-Training of Large Language Models: How to (re)warm your model?.

WSqD: A Horizon-Free Learning Rate Schedule for Large Model Training Continual Pre-Training of Large Language Models: How to (re)warm your model?

Reference 16

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source=arxiv_source observed=2026-07-14T08:04:06.432613Z digest=sha256:9a14d6004c5b0b4d39897d045d461cd07f534a2821492a0153713b56b18a7219

Observation 5ad8840a-331e-46ae-9dbd-8b825817c635 · outbound

This paper cites arXiv preprint arXiv:2510.06826 , year =.

WSqD: A Horizon-Free Learning Rate Schedule for Large Model Training arXiv preprint arXiv:2510.06826 , year =

Reference 17

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source=arxiv_source observed=2026-07-14T08:04:06.432613Z digest=sha256:c495efffcfb0e558aee2e5dab361308310d055f8271e9af98a2d10564edf8e2d

Observation 17f0a578-cff1-475f-b1ad-cad8eb94c624 · outbound

This paper cites Understanding Warmup-Stable-Decay Learning Rates: A River Valley Loss Landscape Perspective.

WSqD: A Horizon-Free Learning Rate Schedule for Large Model Training Understanding Warmup-Stable-Decay Learning Rates: A River Valley Loss Landscape Perspective

Reference 18

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source=arxiv_source observed=2026-07-14T08:04:06.432613Z digest=sha256:f93add903d876ea1ecfd9e6729720d1dc53a6d9ca9d9fde5ad3fc7a797ae09b7

Observation 71b0fb1a-92c2-4bcd-9491-454a0d8c9d4f · outbound

This paper cites The Annals of Mathematical Statistics , volume =.

WSqD: A Horizon-Free Learning Rate Schedule for Large Model Training The Annals of Mathematical Statistics , volume =

Reference 19

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source=arxiv_source observed=2026-07-14T08:04:06.432613Z digest=sha256:e0bd06492d24c660529fe3ee28a3af3080c03b5a7c826943085a3ec499237053

Observation 2f7511f5-d00b-4aa3-9797-17fc6691651e · outbound

This paper cites Foundations and Trends in Optimization , volume =.

WSqD: A Horizon-Free Learning Rate Schedule for Large Model Training Foundations and Trends in Optimization , volume =

Reference 20

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Observation 5e905f5d-ee2a-4f4b-b72c-9e5d0a50e752 · outbound

This paper cites Proceedings of the 30th International Conference on Machine Learning (ICML) , pages =.

WSqD: A Horizon-Free Learning Rate Schedule for Large Model Training Proceedings of the 30th International Conference on Machine Learning (ICML) , pages =

Reference 21

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source=arxiv_source observed=2026-07-14T08:04:06.432613Z digest=sha256:22b161339790cf250e86a2265238c725e0aee3ef64e77ac0f7d4ede9af4e40cc

Observation b1962412-6c91-4623-945b-9096d1bee557 · outbound

This paper cites and Netrapalli, Praneeth , journal =.

WSqD: A Horizon-Free Learning Rate Schedule for Large Model Training and Netrapalli, Praneeth , journal =

Reference 22

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source=arxiv_source observed=2026-07-14T08:04:06.432613Z digest=sha256:144ec8e76c333b58ca8c68b9b8d4ab7a3b828576b2bd63a6487e9f452409e2ea

Observation 0cd15923-1160-4a5e-85b8-0c04dcf74653 · outbound

This paper cites Conference on Learning Theory (COLT) , pages =.

WSqD: A Horizon-Free Learning Rate Schedule for Large Model Training Conference on Learning Theory (COLT) , pages =

Reference 23

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source=arxiv_source observed=2026-07-14T08:04:06.432613Z digest=sha256:01ce646c1d9cd23f420ddc9db23d4be18eb2528fdfa7c3daf69d04b55bbc37ca

Observation 7bf7f6d3-b2f4-49c2-be73-503860a78a89 · outbound

This paper cites SIAM Journal on Control and Optimization , volume =.

WSqD: A Horizon-Free Learning Rate Schedule for Large Model Training SIAM Journal on Control and Optimization , volume =

Reference 24

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source=arxiv_source observed=2026-07-14T08:04:06.432613Z digest=sha256:c51efff576083e65db192b2febd61aaed9a7604f82d01803c065da7431696d08

Observation f6c1aaad-5cf0-4cb8-93fc-a7cb771988b6 · outbound

This paper cites Making Gradient Descent Optimal for Strongly Convex Stochastic Optimization.

WSqD: A Horizon-Free Learning Rate Schedule for Large Model Training Making Gradient Descent Optimal for Strongly Convex Stochastic Optimization

Reference 25

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Observation 2dbc943b-0a39-4915-bc00-c6a07520db28 · outbound

This paper cites IEEE Transactions on Information Theory , volume =.

WSqD: A Horizon-Free Learning Rate Schedule for Large Model Training IEEE Transactions on Information Theory , volume =

Reference 26

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Observation ee9c414d-dfea-4f2b-ae3e-1d58a28ee316 · outbound

This paper cites A simpler approach to obtaining an O(1/t) convergence rate for the projected stochastic subgradient method.

WSqD: A Horizon-Free Learning Rate Schedule for Large Model Training A simpler approach to obtaining an O(1/t) convergence rate for the projected stochastic subgradient method

Reference 27

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source=arxiv_source observed=2026-07-14T08:04:06.432613Z digest=sha256:16ded345811261018f2582c2e878f27106774d824f2557b002a3ea24ec526b21

Observation cb5f67a1-5e0a-4372-af19-7c9fb68b9354 · outbound

This paper cites SIAM Review , volume =.

WSqD: A Horizon-Free Learning Rate Schedule for Large Model Training SIAM Review , volume =

Reference 28

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source=arxiv_source observed=2026-07-14T08:04:06.432613Z digest=sha256:9ac0f942ac0c1ab2df00770dd7c5e2f82b47635df2a55266a1d13dd7c9112c7d

Observation d2ba8398-7a55-46e9-bb4a-cd85f81ba81d · outbound

This paper cites Last Iterate Convergence of.

WSqD: A Horizon-Free Learning Rate Schedule for Large Model Training Last Iterate Convergence of

Reference 29

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source=arxiv_source observed=2026-07-14T08:04:06.432613Z digest=sha256:1f5dbe504df163dbb7d5c6e36079d2255dd328c28c165670e54eeb4d902277d7

Observation ccf42247-756d-468b-a263-914c298aa195 · outbound

This paper cites Scaling Laws for Neural Language Models.

WSqD: A Horizon-Free Learning Rate Schedule for Large Model Training Scaling Laws for Neural Language Models

Reference 30

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source=arxiv_source observed=2026-07-14T08:04:06.432613Z digest=sha256:977fc4e84e31b1935231741f683ed7b9f0b22d6794b2df02f29cc9797b462a83

Observation 56d66542-6c29-4843-a748-1fce2df4d2a5 · outbound

This paper cites Training Compute-Optimal Large Language Models.

WSqD: A Horizon-Free Learning Rate Schedule for Large Model Training Training Compute-Optimal Large Language Models

Reference 31

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source=arxiv_source observed=2026-07-14T08:04:06.432613Z digest=sha256:3c2e8e5ae9ecb76208df1005b5b7e2f296677ae3e336079fab06786ca8c40cba

Observation 66fa0ed2-4a94-41d3-9c0b-55a3e0fec83c · outbound

This paper cites Annual Meeting of the Association for Computational Linguistics , year =.

WSqD: A Horizon-Free Learning Rate Schedule for Large Model Training Annual Meeting of the Association for Computational Linguistics , year =

Reference 32

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source=arxiv_source observed=2026-07-14T08:04:06.432613Z digest=sha256:71fe76f00e8093749fad5b22370a9f7cfff13e8234221e798e0d78a365c2e037

Observation f7c02691-03af-45df-88bc-1725b749afe5 · outbound

This paper cites LLaMA: Open and Efficient Foundation Language Models.

WSqD: A Horizon-Free Learning Rate Schedule for Large Model Training LLaMA: Open and Efficient Foundation Language Models

Reference 33

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source=arxiv_source observed=2026-07-14T08:04:06.432613Z digest=sha256:88f97f5a9ce4dd4b1acbb025684fd6a72db9934f13bd39dbf67dee7a5fafd37d

Observation 99117efc-33eb-4661-9ad6-41741f9055d1 · outbound

This paper cites Advances in Neural Information Processing Systems , year =.

WSqD: A Horizon-Free Learning Rate Schedule for Large Model Training Advances in Neural Information Processing Systems , year =

Reference 34

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Observation 5c2fb8e6-c163-4b17-81f3-c321c283c2e6 · outbound

This paper cites International Conference on Learning Representations , year =.

WSqD: A Horizon-Free Learning Rate Schedule for Large Model Training International Conference on Learning Representations , year =

Reference 35

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Observation fc08684b-5b54-478b-bdbe-8a790e95adc8 · outbound

This paper cites International Conference on Learning Representations , year =.

WSqD: A Horizon-Free Learning Rate Schedule for Large Model Training International Conference on Learning Representations , year =

Reference 36

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source=arxiv_source observed=2026-07-14T08:04:06.432613Z digest=sha256:cadb9cb6c136caf1bd75278155171d930b02d535b319b6add9b3445377703f48

Observation a9510590-6a2c-4f8a-8937-7d82e7444cf3 · outbound

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WSqD: A Horizon-Free Learning Rate Schedule for Large Model Training 2024 , howpublished =

Reference 37

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source=arxiv_source observed=2026-07-14T08:04:06.432613Z digest=sha256:f50065a73463c1c9f9b818555244512a7e8a7d9425e45f549a31870e27477a6c

Observation baaa234d-3af9-45b1-809a-95682d0c0fc2 · outbound

This paper cites Proceedings of the 20th International Conference on Machine Learning (ICML) , pages =.

WSqD: A Horizon-Free Learning Rate Schedule for Large Model Training Proceedings of the 20th International Conference on Machine Learning (ICML) , pages =

Reference 38

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source=arxiv_source observed=2026-07-14T08:04:06.432613Z digest=sha256:9ce20285693b957b3a6515517c1a324a99f0a925bc70b2387c2160d90444a47e

Observation bdc9e83c-8412-466a-b5ea-b623a01f46dd · outbound

This paper cites and Hestness, Joel and Dey, Nolan , year =.

WSqD: A Horizon-Free Learning Rate Schedule for Large Model Training and Hestness, Joel and Dey, Nolan , year =

Reference 39

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source=arxiv_source observed=2026-07-14T08:04:06.432613Z digest=sha256:3fb5a742ab453366fde422cc24623a1be8daa9285b99ca10c3420390fa0cb6a8

Observation c8ef9a40-a4c0-430a-b21e-0619318d037c · outbound

This paper cites Straight to Zero: Why Linearly Decaying the Learning Rate to Zero Works Best for.

WSqD: A Horizon-Free Learning Rate Schedule for Large Model Training Straight to Zero: Why Linearly Decaying the Learning Rate to Zero Works Best for

Reference 40

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source=arxiv_source observed=2026-07-14T08:04:06.432613Z digest=sha256:d66a477056d4d9a4e374d9a8a837153964448d9122725bd28ad8bccaa5e7740f

Observation b99f9a30-c066-4cd1-9316-c5cc3d4418c4 · outbound

This paper cites arXiv preprint arXiv:2602.03702 , year=.

WSqD: A Horizon-Free Learning Rate Schedule for Large Model Training arXiv preprint arXiv:2602.03702 , year=

Reference 41

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source=arxiv_source observed=2026-07-14T08:04:06.432613Z digest=sha256:e8e34a697e53c143d860f5ecc200731b49671750249a0fd5fed46d8660cc1726

Observation ccfc6571-ac51-4922-8f6a-f16fab80f584 · outbound

This paper cites International Conference on Learning Representations , year =.

WSqD: A Horizon-Free Learning Rate Schedule for Large Model Training International Conference on Learning Representations , year =

Reference 42

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source=arxiv_source observed=2026-07-14T08:04:06.432613Z digest=sha256:04993b097a37531d947ab396459af65efd73938c0d3f6dfbcbbc4f265059f0fe

Observation af73a82f-de29-4809-a012-b7bcdcefd012 · outbound

This paper cites arXiv preprint arXiv:2312.08531 , year=.

WSqD: A Horizon-Free Learning Rate Schedule for Large Model Training arXiv preprint arXiv:2312.08531 , year=

Reference 43

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source=arxiv_source observed=2026-07-14T08:04:06.432613Z digest=sha256:fd16d4d83f51905c655519b0c2f04570a3e074cbad911035eb3d0867235b7692

Observation 948419de-a8c6-4fbe-9f0a-1ea0c86e0fe9 · outbound

This paper cites Old Optimizer, New Norm: An Anthology.

WSqD: A Horizon-Free Learning Rate Schedule for Large Model Training Old Optimizer, New Norm: An Anthology

Reference 44

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source=arxiv_source observed=2026-07-14T08:04:06.432613Z digest=sha256:c79bebe4d3799720b22a6a681ae501a3ddb76e8d81bb8161cb3f60e4b051ad70

Observation 87452c44-f0f7-4b6a-93db-d6a4e860b88c · outbound

This paper cites Anytime Training with Schedule-Free Spectral Optimization.

WSqD: A Horizon-Free Learning Rate Schedule for Large Model Training Anytime Training with Schedule-Free Spectral Optimization

Reference 45

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source=arxiv_source observed=2026-07-14T08:04:06.432613Z digest=sha256:b174ddf77628c86fc2e2b193a552d9e992bebf22ef8469bc4d8f254a5d88004d

Observation 4899f82c-5462-4ba3-8c4b-3f31964622b5 · outbound

This paper cites Gradient Descent's Last Iterate is Often (slightly) Suboptimal.

WSqD: A Horizon-Free Learning Rate Schedule for Large Model Training Gradient Descent's Last Iterate is Often (slightly) Suboptimal

Reference 46

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source=arxiv_source observed=2026-07-14T08:04:06.432613Z digest=sha256:677276be40f7aa1622cf9fff02dcaac7b6c0930c71d81406ba32e5b316b8c668

Observation 82b592df-f960-4faa-aa55-54c321cbcdb7 · outbound

This paper cites Reuse, Don't Retrain: A Recipe for Continued Pretraining of Language Models.

WSqD: A Horizon-Free Learning Rate Schedule for Large Model Training Reuse, Don't Retrain: A Recipe for Continued Pretraining of Language Models

Reference 47

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source=arxiv_source observed=2026-07-14T08:04:06.432613Z digest=sha256:4994be8216bfd450b3811db98aff7b77e23162df5e41f560d234e95e8ce2ddd1

Observation bc8022f5-37a3-44bd-854d-71f1e4c25e2c · outbound

This paper cites Power Scheduler: A Batch Size and Token Number Agnostic Learning Rate Scheduler.

WSqD: A Horizon-Free Learning Rate Schedule for Large Model Training Power Scheduler: A Batch Size and Token Number Agnostic Learning Rate Scheduler

Reference 48

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source=arxiv_source observed=2026-07-14T08:04:06.432613Z digest=sha256:89a8332ae403e6c379328d9d434dafbe681ec56bd60d9886a292b2f8aefe5c19

Observation 17bc5f56-7e6d-4021-90d4-5034dd79dba3 · outbound

This paper cites Beyond Cosine Decay: On the effectiveness of Infinite Learning Rate Schedule for Continual Pre-training.

WSqD: A Horizon-Free Learning Rate Schedule for Large Model Training Beyond Cosine Decay: On the effectiveness of Infinite Learning Rate Schedule for Continual Pre-training

Reference 49

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source=arxiv_source observed=2026-07-14T08:04:06.432613Z digest=sha256:b84ed86eb3b28b6e942f1ddf3d57a7fb86cf745882c25b552b34167b02c817db

Observation 946f17a8-297a-41de-843e-b5edf6e0d030 · outbound

This paper cites WSM: Decay-Free Learning Rate Schedule via Checkpoint Merging for LLM Pre-training.

WSqD: A Horizon-Free Learning Rate Schedule for Large Model Training WSM: Decay-Free Learning Rate Schedule via Checkpoint Merging for LLM Pre-training

Reference 50

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source=arxiv_source observed=2026-07-14T08:04:06.432613Z digest=sha256:71834f29db1480739d3aa9deffe852b117bdffddb322ea913faf73a55466dbe0

Observation 208cfa66-0cc2-4c6d-925f-0c2d559ff9b5 · outbound

This paper cites Efficient Continual Pre-training by Mitigating the Stability Gap.

WSqD: A Horizon-Free Learning Rate Schedule for Large Model Training Efficient Continual Pre-training by Mitigating the Stability Gap

Reference 51

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source=arxiv_source observed=2026-07-14T08:04:06.432613Z digest=sha256:ff36ea91ee3550e374046c40679cc1b57d66a19ebbc4028843c03657f5743786

Observation ea9a4ce6-bb20-4ccf-b116-ea5ddf8f6584 · outbound

This paper cites International Conference on Learning Representations (ICLR) , year =.

WSqD: A Horizon-Free Learning Rate Schedule for Large Model Training International Conference on Learning Representations (ICLR) , year =

Reference 52

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source=arxiv_source observed=2026-07-14T08:04:06.432613Z digest=sha256:f3fca31fbfe2069ddf7b7db4973162101ed4abbc53b9a2142227ad25ac5cfac3

Observation 2b84bfe1-2116-411a-b4fe-13fde4b8f7f5 · outbound

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

WSqD: A Horizon-Free Learning Rate Schedule for Large Model Training The Surprising Agreement Between Convex Optimization Theory and Learning-Rate Scheduling for Large Model Training

Reference 53

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source=arxiv_source observed=2026-07-14T08:04:06.432613Z digest=sha256:4c9a0579305b0e5aa8449623f38c114ee12b69da0f73dd351863c24574ece803

Observation 59937c91-802e-4155-afeb-3c5d252efd55 · outbound

This paper cites Taking the Road Less Scheduled with Adaptive Polyak Steps.

WSqD: A Horizon-Free Learning Rate Schedule for Large Model Training Taking the Road Less Scheduled with Adaptive Polyak Steps

Reference 54

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source=arxiv_source observed=2026-07-14T08:04:06.432613Z digest=sha256:149f9bbb1a04f267243ce743db2118a4cfdc27ad9143fdf325c220a3fb79dcb2

Observation cf372b36-f076-4953-8a3b-53e2690c4e55 · outbound

This paper cites 2 OLMo 2 Furious.

WSqD: A Horizon-Free Learning Rate Schedule for Large Model Training 2 OLMo 2 Furious

Reference 55

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source=arxiv_source observed=2026-07-14T08:04:06.432613Z digest=sha256:f4beaca8995050f3db04d5ebcfa52ae5bcc154492e2660b479bd0b8286e6f219

Observation 4557f549-0212-40f7-b244-df88b88eb25f · outbound

This paper cites Scaling Optimal.

WSqD: A Horizon-Free Learning Rate Schedule for Large Model Training Scaling Optimal

Reference 56

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source=arxiv_source observed=2026-07-14T08:04:06.432613Z digest=sha256:48a67caa60ee7ffa7eb884f9e928442aee99dbb9a2bc80e8cf28368211b23011

Observation 486dd93a-4082-488e-92f4-b365dd16e2b6 · outbound

This paper cites International Conference on Machine Learning (ICML) , year =.

WSqD: A Horizon-Free Learning Rate Schedule for Large Model Training International Conference on Machine Learning (ICML) , year =

Reference 57

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source=arxiv_source observed=2026-07-14T08:04:06.432613Z digest=sha256:d37ca50e7f372068157fda3b576e41a2cb385a611d42af5100f2c0f7a44f6c33

Observation 92f29c48-c542-4454-83ec-796acd8a34ee · outbound

This paper cites Early Weight Averaging Meets High Learning Rates for.

WSqD: A Horizon-Free Learning Rate Schedule for Large Model Training Early Weight Averaging Meets High Learning Rates for

Reference 58

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source=arxiv_source observed=2026-07-14T08:04:06.432613Z digest=sha256:a9c8eadb31e8494c94f3a86a58d2679ee3c66fbe9d451b5789d1b428196d2e1d

Observation 5519170b-8034-4edf-8eb0-c05edc37795f · outbound

This paper cites Mpemba Effect in Large-Language Model Training Dynamics: A Minimal Analysis of the Valley-River model.

WSqD: A Horizon-Free Learning Rate Schedule for Large Model Training Mpemba Effect in Large-Language Model Training Dynamics: A Minimal Analysis of the Valley-River model

Reference 59

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source=arxiv_source observed=2026-07-14T08:04:06.432613Z digest=sha256:398651472e91d6dfabd777fc80925367813e8cff1126d05be5988125ab26e480

Observation 6f0df9ef-db9d-417e-a1d8-41ecbd3102e0 · outbound

This paper cites Advances in Neural Information Processing Systems (NeurIPS) , year =.

WSqD: A Horizon-Free Learning Rate Schedule for Large Model Training Advances in Neural Information Processing Systems (NeurIPS) , year =

Reference 60

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source=arxiv_source observed=2026-07-14T08:04:06.432613Z digest=sha256:3170041d3d93b67d018665236aeaab218ee66cd90b57fee8907bb415ed9b6ed0

Observation 8bb58ada-5e7b-485a-bd57-888d158d446c · outbound

This paper cites International Conference on Learning Representations (ICLR) , year =.

WSqD: A Horizon-Free Learning Rate Schedule for Large Model Training International Conference on Learning Representations (ICLR) , year =

Reference 61

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source=arxiv_source observed=2026-07-14T08:04:06.432613Z digest=sha256:08d010a118a5930301450a2bdb17c34893075834fb2024f47e44cf5163eaba25

Pith citing papers

No inbound Pith citation observations are available.