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Paper Citation Record · LEDGER

Characterization and Mitigation of Training Instabilities in Microscaling Formats

As of 10 August 2026, this Paper Citation Record lists 47 of 47 outbound references and 2 inbound Pith citation observations for arXiv:2506.20752.

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

pith.paper-citation-record.v1
2506.20752 v1

Coverage vector

measured 47 of 47 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T22:47:52.291301Z

measured 49 of 49 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-02T18:45:23.162011Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-20T07:03:23.229274Z

Reference resolution

47 of 47 outbound references displayed

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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 4d5e57a5-33d2-4c06-adea-adcbf60450da · outbound

This paper cites an unresolved cited work.

Characterization and Mitigation of Training Instabilities in Microscaling Formats Unresolved cited work

Reference 1

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This paper cites Claude 4 system card.

Characterization and Mitigation of Training Instabilities in Microscaling Formats Claude 4 system card

Reference 2

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Observation 65ecfcc5-d7f2-4290-b723-ff47c41ded32 · outbound

This paper cites an unresolved cited work.

Characterization and Mitigation of Training Instabilities in Microscaling Formats Unresolved cited work

Reference 3

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Observation 7d601040-612c-4bf7-82fb-444c1c6d7747 · outbound

This paper cites Loss-to-Loss Prediction: Scaling Laws for All Datasets.

Characterization and Mitigation of Training Instabilities in Microscaling Formats Loss-to-Loss Prediction: Scaling Laws for All Datasets

Reference 4

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Observation 82c1a175-9bc8-45d4-9599-1eea8c28cbec · outbound

This paper cites an unresolved cited work.

Characterization and Mitigation of Training Instabilities in Microscaling Formats Unresolved cited work

Reference 5

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Characterization and Mitigation of Training Instabilities in Microscaling Formats Unresolved cited work

Reference 6

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This paper cites Z., and Talwalkar, A.

Characterization and Mitigation of Training Instabilities in Microscaling Formats Z., and Talwalkar, A

Reference 7

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Observation 9b0436d0-9ea0-4d34-8458-66958def1a76 · outbound

This paper cites A., Cao, C., Cao, K., Castagné, R., Cendrero, J., Currie, L.

Characterization and Mitigation of Training Instabilities in Microscaling Formats A., Cao, C., Cao, K., Castagné, R., Cendrero, J., Currie, L

Reference 8

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Characterization and Mitigation of Training Instabilities in Microscaling Formats Unresolved cited work

Reference 9

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Characterization and Mitigation of Training Instabilities in Microscaling Formats Unresolved cited work

Reference 10

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This paper cites an unresolved cited work.

Characterization and Mitigation of Training Instabilities in Microscaling Formats Unresolved cited work

Reference 11

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This paper cites an unresolved cited work.

Characterization and Mitigation of Training Instabilities in Microscaling Formats Unresolved cited work

Reference 12

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This paper cites F., Mahendran, A., Yu, F., Oliver, A., Huot, F., Bastings, J., Collier, M.

Characterization and Mitigation of Training Instabilities in Microscaling Formats F., Mahendran, A., Yu, F., Oliver, A., Huot, F., Bastings, J., Collier, M

Reference 13

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Characterization and Mitigation of Training Instabilities in Microscaling Formats and Zettlemoyer, L

Reference 14

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Observation 87602fef-a9ad-4628-a28a-e57900c9ba43 · outbound

This paper cites Scaling FP8 training to trillion-token LLMs.

Characterization and Mitigation of Training Instabilities in Microscaling Formats Scaling FP8 training to trillion-token LLMs

Reference 15

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Source-reported events for the cited work

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Observation 6255c346-f326-4941-93eb-2aca0faa2205 · outbound

This paper cites The Llama 3 Herd of Models.

Characterization and Mitigation of Training Instabilities in Microscaling Formats The Llama 3 Herd of Models

Reference 16

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Observation 185eae80-d5ff-47b0-bfbb-77c271ce78c2 · outbound

This paper cites OLMo: Accelerating the Science of Language Models.

Characterization and Mitigation of Training Instabilities in Microscaling Formats OLMo: Accelerating the Science of Language Models

Reference 17

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Observation 23484004-c0c6-42a9-960c-3ca010aaacb5 · outbound

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Characterization and Mitigation of Training Instabilities in Microscaling Formats Query-Key Normalization for Transformers

Reference 18

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Characterization and Mitigation of Training Instabilities in Microscaling Formats Training Compute-Optimal Large Language Models

Reference 19

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Characterization and Mitigation of Training Instabilities in Microscaling Formats L., Anthony, Q

Reference 20

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Characterization and Mitigation of Training Instabilities in Microscaling Formats Unresolved cited work

Reference 21

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Characterization and Mitigation of Training Instabilities in Microscaling Formats Unresolved cited work

Reference 22

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Characterization and Mitigation of Training Instabilities in Microscaling Formats Scaling Laws for Neural Language Models

Reference 23

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Observation 4380e891-2ae2-4b64-a401-36da515700d0 · outbound

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Characterization and Mitigation of Training Instabilities in Microscaling Formats Scaling Laws for Precision

Reference 24

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Characterization and Mitigation of Training Instabilities in Microscaling Formats J., and Lee, D

Reference 25

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Characterization and Mitigation of Training Instabilities in Microscaling Formats DeepSeek-V3 Technical Report

Reference 26

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Characterization and Mitigation of Training Instabilities in Microscaling Formats Unresolved cited work

Reference 27

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Characterization and Mitigation of Training Instabilities in Microscaling Formats Unresolved cited work

Reference 28

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Characterization and Mitigation of Training Instabilities in Microscaling Formats Unresolved cited work

Reference 29

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Characterization and Mitigation of Training Instabilities in Microscaling Formats FP8 Formats for Deep Learning

Reference 30

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Characterization and Mitigation of Training Instabilities in Microscaling Formats Unresolved cited work

Reference 31

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Characterization and Mitigation of Training Instabilities in Microscaling Formats Mx pytorch emulation library

Reference 32

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This paper cites S., Narang, S., Poulton, A., Silva, R., Tang, B., Liskovich, D., Xu, P., Zhang, Y., Kambadur, M., Roller, S., and Zhang, S.

Characterization and Mitigation of Training Instabilities in Microscaling Formats S., Narang, S., Poulton, A., Silva, R., Tang, B., Liskovich, D., Xu, P., Zhang, Y., Kambadur, M., Roller, S., and Zhang, S

Reference 33

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Source-reported events for the cited work

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Characterization and Mitigation of Training Instabilities in Microscaling Formats Nvidia blackwell architecture

Reference 34

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Characterization and Mitigation of Training Instabilities in Microscaling Formats Gpt-4.5 system card

Reference 35

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This paper cites Low-Bit Quantization Favors Undertrained LLMs: Scaling Laws for Quantized LLMs with 100T Training Tokens.

Characterization and Mitigation of Training Instabilities in Microscaling Formats Low-Bit Quantization Favors Undertrained LLMs: Scaling Laws for Quantized LLMs with 100T Training Tokens

Reference 36

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Source-reported events for the cited work

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Observation 18a9aeec-c1f6-42af-8e75-63d1a495d27f · outbound

This paper cites B., Lozhkov, A., Mitchell, M., Raffel, C., Werra, L.

Characterization and Mitigation of Training Instabilities in Microscaling Formats B., Lozhkov, A., Mitchell, M., Raffel, C., Werra, L

Reference 37

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Reference 38

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Characterization and Mitigation of Training Instabilities in Microscaling Formats Microscaling Data Formats for Deep Learning

Reference 39

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Reference 40

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Reference 41

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Characterization and Mitigation of Training Instabilities in Microscaling Formats Massive Activations in Large Language Models

Reference 42

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Reference 43

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Characterization and Mitigation of Training Instabilities in Microscaling Formats J., Xiao, L., Everett, K

Reference 44

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Reference 45

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Characterization and Mitigation of Training Instabilities in Microscaling Formats V., Mihaylov, T., Ott, M., Shleifer, S., Shuster, K., Simig, D., Koura, P

Reference 46

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Pith citing papers

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Preventing Learning Stagnation in PPO by Scaling to 1 Million Parallel Environments cites this paper.

Preventing Learning Stagnation in PPO by Scaling to 1 Million Parallel Environments Characterization and Mitigation of Training Instabilities in Microscaling Formats

Reference 56

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The Thermodynamic Costs of Simple Linear Regression cites this paper.

The Thermodynamic Costs of Simple Linear Regression Characterization and Mitigation of Training Instabilities in Microscaling Formats

Reference 48

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