Pith. sign in

Paper Citation Record · LEDGER

Scaling Laws and Compute-Optimal Training Beyond Fixed Training Durations

As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 19 inbound Pith citation observations for arXiv:2405.18392.

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

pith.paper-citation-record.v1
2405.18392 v3

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 19 of 19 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 19 of 19 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-08T20:06:23.521445Z

measured 1 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Reference resolution

0 of 0 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved0
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

2
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 5c6e8e3e-a02d-4d8b-959e-cbcb79d340f2 · inbound

Optimization Hyper-parameter Laws for Large Language Models cites this paper.

Optimization Hyper-parameter Laws for Large Language Models Scaling Laws and Compute-Optimal Training Beyond Fixed Training Durations

Reference 14

Resolution
verified exact
arxiv_id, observed 2026-05-23T20:45:48.861142Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-05-23T20:45:31.427677Z digest=sha256:15dbe49bb482ed0124eafc3c7e3aa9dbedf6cfd73d577dabc6df00c9afe9aaa2

Observation d8d53b71-3169-4cce-ae0e-ce8fad15b0d4 · inbound

Smarter, Better, Faster, Longer: A Modern Bidirectional Encoder for Fast, Memory Efficient, and Long Context Finetuning and Inference cites this paper.

Smarter, Better, Faster, Longer: A Modern Bidirectional Encoder for Fast, Memory Efficient, and Long Context Finetuning and Inference Scaling Laws and Compute-Optimal Training Beyond Fixed Training Durations

Reference 141

Resolution
verified exact
arxiv_id, observed 2026-05-20T17:46:46.973215Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-05-20T17:46:46.845424Z digest=sha256:04f53f6864405f7eec627fc93aa2bf53754b9cee73f7ea32f8487845353a6534

Observation 9faea4ab-6f22-4e48-8bd1-ffc020328510 · inbound

Smarter, Better, Faster, Longer: A Modern Bidirectional Encoder for Fast, Memory Efficient, and Long Context Finetuning and Inference cites this paper.

Smarter, Better, Faster, Longer: A Modern Bidirectional Encoder for Fast, Memory Efficient, and Long Context Finetuning and Inference Scaling Laws and Compute-Optimal Training Beyond Fixed Training Durations

Reference 148

Resolution
verified exact
arxiv_id, observed 2026-05-20T17:46:47.063757Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-05-20T17:46:46.845424Z digest=sha256:c8ee1e25843537c986e54e466f30615cd66bfd6975d001b9e2d4568b6a12cdd8

Observation 8481af53-3f09-4cf1-b860-802cd22b8d79 · inbound

SmolLM2: When Smol Goes Big -- Data-Centric Training of a Small Language Model cites this paper.

SmolLM2: When Smol Goes Big -- Data-Centric Training of a Small Language Model Scaling Laws and Compute-Optimal Training Beyond Fixed Training Durations

Reference 177

Resolution
metadata mismatch
arxiv_id, observed 2026-05-13T17:30:02.959073Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-05-13T17:30:02.803757Z digest=sha256:af8579139c377311b1e781091558d7eed9fcaf372843dfc3b82d55c38b154905

Observation 50ca0f07-5562-4a8f-9e7f-74178882b260 · inbound

Joint MoE Scaling Laws: Mixture of Experts Can Be Memory Efficient cites this paper.

Joint MoE Scaling Laws: Mixture of Experts Can Be Memory Efficient Scaling Laws and Compute-Optimal Training Beyond Fixed Training Durations

Reference 17

Resolution
unresolved
no resolver link, observed 2026-08-08T20:06:23.521445Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-08T20:06:23.521445Z digest=sha256:0a2adc4bb8ec8d9981e8d4b5b2f3cda10284d8607d4f707d7fb690e29bf7b506

Observation c1428323-a73c-4f23-8e36-d0a5ebcdb450 · inbound

BioClinical ModernBERT: A State-of-the-Art Long-Context Encoder for Biomedical and Clinical NLP cites this paper.

BioClinical ModernBERT: A State-of-the-Art Long-Context Encoder for Biomedical and Clinical NLP Scaling Laws and Compute-Optimal Training Beyond Fixed Training Durations

Reference 22

Resolution
unresolved
no resolver link, observed 2026-08-07T04:21:41.884536Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T04:21:41.884536Z digest=sha256:70bac284f37c39e620a359028c20ccc2f1612e53e67259e56aa0a1e34fba0d1a

Observation bbf646a2-602d-4105-bf75-187a47e1043a · inbound

Is your batch size the problem? Revisiting the Adam-SGD gap in language modeling cites this paper.

Is your batch size the problem? Revisiting the Adam-SGD gap in language modeling Scaling Laws and Compute-Optimal Training Beyond Fixed Training Durations

Reference 9

Resolution
unresolved
no resolver link, observed 2026-08-07T00:50:30.555421Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T00:50:30.555421Z digest=sha256:58c51b399e0aa688ecce55d34e76f496ae4c26476aff57f7e295313f46f3e227

Observation a237938b-8a27-45c8-befd-83775802303a · inbound

The Automated LLM Speedrunning Benchmark: Reproducing NanoGPT Improvements cites this paper.

The Automated LLM Speedrunning Benchmark: Reproducing NanoGPT Improvements Scaling Laws and Compute-Optimal Training Beyond Fixed Training Durations

Reference 18

Resolution
unresolved
no resolver link, observed 2026-08-06T22:10:24.529827Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T22:10:24.529827Z digest=sha256:6d156df2807024f422b7abd38cc88d373a76f24bf1bda322f3bf8963c79208d2

Observation a50d57de-612e-4e2e-b028-99df93492290 · inbound

AbbIE: Autoregressive Block-Based Iterative Encoder for Efficient Sequence Modeling cites this paper.

AbbIE: Autoregressive Block-Based Iterative Encoder for Efficient Sequence Modeling Scaling Laws and Compute-Optimal Training Beyond Fixed Training Durations

Reference 26

Resolution
unresolved
no resolver link, observed 2026-08-06T18:24:51.960524Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T18:24:51.960524Z digest=sha256:534a4d9b2be2ab1b3148ad7f5962f841cd8987ced3ba30bfdd0526041f08c9cc

Observation 25ab7824-863b-428a-92a9-768cb60b62eb · inbound

Analysis of Schedule-Free Nonconvex Optimization cites this paper.

Analysis of Schedule-Free Nonconvex Optimization Scaling Laws and Compute-Optimal Training Beyond Fixed Training Durations

Reference 22

Resolution
unresolved
no resolver link, observed 2026-08-05T22:42:10.446190Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T22:42:10.446190Z digest=sha256:317059dd1c987b4ae67d389bba27deb052cf2ee9a81c65dbd0ff3754218ce526

Observation f919b332-930b-47ca-94f3-762fe9bce088 · inbound

Foundation Models for Discovery and Exploration in Chemical Space cites this paper.

Foundation Models for Discovery and Exploration in Chemical Space Scaling Laws and Compute-Optimal Training Beyond Fixed Training Durations

Reference 281

Resolution
verified exact
arxiv_id, observed 2026-05-18T05:52:24.910606Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-05-18T05:52:10.848118Z digest=sha256:c7b38bd39e45c6ff5dbc242e2ab839d046e180bedd16a05b91618477209495e6

Observation faa0eda6-b091-4192-941c-e878bdf1ea6c · inbound

Scaling-Aware Data Selection for End-to-End Autonomous Driving Systems cites this paper.

Scaling-Aware Data Selection for End-to-End Autonomous Driving Systems Scaling Laws and Compute-Optimal Training Beyond Fixed Training Durations

Reference 20

Resolution
metadata mismatch
arxiv_id, observed 2026-05-11T06:56:02.214483Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-05-10T17:22:25.943097Z digest=sha256:e7014ef47c95ac76372a8959fd4ea5dbc5c9e41acc32c994fd6db90a4e51b82a

Observation 0d3a9710-b345-4969-9776-c3b3da0ec585 · inbound

Scaling Laws for Mixture Pretraining Under Data Constraints cites this paper.

Scaling Laws for Mixture Pretraining Under Data Constraints Scaling Laws and Compute-Optimal Training Beyond Fixed Training Durations

Reference 48

Resolution
metadata mismatch
arxiv_id, observed 2026-05-14T21:48:00.932636Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-05-14T21:44:31.429223Z digest=sha256:6af4bbb88bb1292f42da835bc390148e2a3521218d61ddfb54a3c82856297501

Observation 1c0c4aa7-a990-4f60-8fe7-430a2084560b · inbound

Mix, Don't Tune: Bilingual Pre-Training Outperforms Hyperparameter Search in Data-Constrained Settings cites this paper.

Mix, Don't Tune: Bilingual Pre-Training Outperforms Hyperparameter Search in Data-Constrained Settings Scaling Laws and Compute-Optimal Training Beyond Fixed Training Durations

Reference 9

Resolution
metadata mismatch
arxiv_id, observed 2026-05-14T20:19:27.369790Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-05-14T20:17:26.661595Z digest=sha256:390c16cb539366244766939062a6f22bc0b0725bd9a383f9a9bedfe8ebc98209

Observation bc7b8a2f-2565-494d-a659-9a50a5bd435c · inbound

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

Anytime Training with Schedule-Free Spectral Optimization Scaling Laws and Compute-Optimal Training Beyond Fixed Training Durations

Reference 26

Resolution
verified exact
arxiv_id, observed 2026-05-25T05:40:24.418964Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-05-25T05:38:16.958574Z digest=sha256:3f66e7f9a56a87c81c178029eac5dba2f92dd81105235d614190e9035f6660ca

Observation 96f3dcb0-b37b-470b-a982-57501ec69192 · inbound

Mellum2 Technical Report cites this paper.

Mellum2 Technical Report Scaling Laws and Compute-Optimal Training Beyond Fixed Training Durations

Reference 25

Resolution
metadata mismatch
arxiv_id, observed 2026-06-28T23:02:46.500620Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-06-28T22:58:35.397914Z digest=sha256:5ae7e9d15c99a2b3b49d1498977d69ca5841a333470c5ab41eb9c35b28554243

Observation 137949ea-f578-451a-b46b-ffe82b532b32 · inbound

Improving Neural Network Training by Decoupling the Magnitude and Direction of Weight Vectors cites this paper.

Improving Neural Network Training by Decoupling the Magnitude and Direction of Weight Vectors Scaling Laws and Compute-Optimal Training Beyond Fixed Training Durations

Reference 103

Resolution
metadata mismatch
arxiv_id, observed 2026-07-04T20:30:07.784468Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-06-25T20:05:09.179627Z digest=sha256:c06a725072b55d12917bb3289072dd7fcf457119124ab4dce312c65a8c49b2b4

Observation 605173fd-8a1f-4072-8768-fee7b95d5b2d · inbound

Improving Neural Network Training by Decoupling the Magnitude and Direction of Weight Vectors cites this paper.

Improving Neural Network Training by Decoupling the Magnitude and Direction of Weight Vectors Scaling Laws and Compute-Optimal Training Beyond Fixed Training Durations

Reference 27

Resolution
unresolved
no resolver link, observed 2026-08-02T10:14:06.866381Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-02T10:14:06.866381Z digest=sha256:91f426e78db3d05f610742783006e4abb0d53950a74dbd52a50808ff88d350cc

Observation db6076a0-f5b2-49bf-89e4-fff2dbae6c71 · inbound

Scaling Point-in-Time Language Models cites this paper.

Scaling Point-in-Time Language Models Scaling Laws and Compute-Optimal Training Beyond Fixed Training Durations

Reference 2018

Resolution
unresolved
no resolver link, observed 2026-08-02T15:39:37.355927Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-02T15:39:37.355927Z digest=sha256:946830ca8e4b3daa66c018c93e341139d890ded4f136605e07d7ffdbfc0c3644