Paper Citation Record · LEDGER

Analyzing & Reducing the Need for Learning Rate Warmup in GPT Training

As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:2410.23922.

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

pith.paper-citation-record.v1
2410.23922 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 4 of 4 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00

measured 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T10:58:33.177144Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-04T20:30:07.747085Z

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 7665e3b4-2735-47bf-af79-9905b2f705cf · inbound

Lions and Muons: Optimization via Stochastic Frank-Wolfe under Heavy-Tailed Noise cites this paper.

Lions and Muons: Optimization via Stochastic Frank-Wolfe under Heavy-Tailed Noise Analyzing & Reducing the Need for Learning Rate Warmup in GPT Training

Reference 48

Resolution: unresolved; no resolver link, observed 2026-08-07T10:58:33.177144Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9aac8c65-2157-402a-bbc4-ac7fd291b559 · inbound

Demystifying Manifold Constraints in LLM Pre-training cites this paper.

Demystifying Manifold Constraints in LLM Pre-training Analyzing & Reducing the Need for Learning Rate Warmup in GPT Training

Reference 61

Resolution: verified exact; arxiv_id, observed 2026-05-11T17:16:08.665048Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation ac1851fc-5b7f-42d5-aec4-97eadcd7e670 · 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 Analyzing & Reducing the Need for Learning Rate Warmup in GPT Training

Reference 108

Resolution: metadata mismatch; arxiv_id, observed 2026-07-04T20:30:07.748921Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 9353a1b6-8a37-47c6-a753-40d276ef398a · 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 Analyzing & Reducing the Need for Learning Rate Warmup in GPT Training

Reference 44

Resolution: unresolved; no resolver link, observed 2026-08-02T10:14:08.282524Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.