Paper Citation Record · LEDGER

Scale Efficiently: Insights from Pre-training and Fine-tuning Transformers

As of 4 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 8 inbound Pith citation observations for arXiv:2109.10686.

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

pith.paper-citation-record.v1
2109.10686 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 8 of 8 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-04T06:34:03.388597+00:00

measured 8 of 8 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-04T09:14:46.158438Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-03T20:58:58.624838Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation f5c8e044-8d91-4c13-8ae6-394d6e6722d2 · inbound

ST-MoE: Designing Stable and Transferable Sparse Expert Models cites this paper.

ST-MoE: Designing Stable and Transferable Sparse Expert Models Scale Efficiently: Insights from Pre-training and Fine-tuning Transformers

Reference 203

Resolution: verified exact; arxiv_id, observed 2026-05-12T23:14:25.884221Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 7cbb63ad-5a56-42f2-8554-9bf072061909 · inbound

BloombergGPT: A Large Language Model for Finance cites this paper.

BloombergGPT: A Large Language Model for Finance Scale Efficiently: Insights from Pre-training and Fine-tuning Transformers

Reference 114

Resolution: verified exact; arxiv_id, observed 2026-05-13T23:19:46.822979Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 5bdfa959-e310-4485-baac-dd5456fe053f · inbound

Scaling Data-Constrained Language Models cites this paper.

Scaling Data-Constrained Language Models Scale Efficiently: Insights from Pre-training and Fine-tuning Transformers

Reference 113

Resolution: verified exact; arxiv_id, observed 2026-05-18T01:35:21.396172Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 4ac6baf2-3cc7-48da-a810-1e916a881b4e · inbound

Chronos: Learning the Language of Time Series cites this paper.

Chronos: Learning the Language of Time Series Scale Efficiently: Insights from Pre-training and Fine-tuning Transformers

Reference 83

Resolution: verified exact; arxiv_id, observed 2026-05-13T08:27:23.392866Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 573986e9-fc42-4169-9db4-cb78c5561e47 · inbound

Large-Scale AI and Foundation Models for Neuroscience: A Comprehensive Review cites this paper.

Large-Scale AI and Foundation Models for Neuroscience: A Comprehensive Review Scale Efficiently: Insights from Pre-training and Fine-tuning Transformers

Reference 67

Resolution: unresolved; no resolver link, observed 2026-08-04T09:14:46.158438Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ad6a4eb1-b724-46c8-9c8d-59507364db17 · inbound

Scaling Laws Meet Model Architecture: Toward Inference-Efficient LLMs cites this paper.

Scaling Laws Meet Model Architecture: Toward Inference-Efficient LLMs Scale Efficiently: Insights from Pre-training and Fine-tuning Transformers

Reference 39

Resolution: verified exact; arxiv_id, observed 2026-05-18T05:30:55.097385Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 15cfc1ff-6bfc-4712-9e17-e679d507a60f · inbound

Large-scale Codec Avatars: The Unreasonable Effectiveness of Large-scale Avatar Pretraining cites this paper.

Large-scale Codec Avatars: The Unreasonable Effectiveness of Large-scale Avatar Pretraining Scale Efficiently: Insights from Pre-training and Fine-tuning Transformers

Reference 59

Resolution: verified exact; arxiv_id, observed 2026-05-13T21:33:18.336782Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 93b62383-9748-40ad-aebb-e83093829b24 · inbound

Variable-Width Transformers cites this paper.

Variable-Width Transformers Scale Efficiently: Insights from Pre-training and Fine-tuning Transformers

Reference 39

Resolution: metadata mismatch; arxiv_id, observed 2026-07-03T20:58:58.626372Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.