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Paper Citation Record · LEDGER

The Stability-Efficiency Dilemma: Investigating Sequence Length Warmup for Training GPT Models

As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2108.06084.

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

pith.paper-citation-record.v1
2108.06084 v4

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 2 of 2 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 2026-08-07T15:19:26.765813Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-22T21:15:09.614618Z

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 75d63b07-ed97-4d91-b8e8-87f72239ce93 · inbound

MegaScale-Data: Scaling Dataloader for Multisource Large Foundation Model Training cites this paper.

MegaScale-Data: Scaling Dataloader for Multisource Large Foundation Model Training The Stability-Efficiency Dilemma: Investigating Sequence Length Warmup for Training GPT Models

Reference 43

Resolution
verified exact
arxiv_id, observed 2026-05-22T21:15:09.617034Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-05-22T21:12:22.201810Z digest=sha256:eb16bcf683b20435e1cddf8e171e5bf6dbea2260817ef07afb1ede46dc1b7fb9

Observation 760a3f7b-41bf-4277-b85a-4857b90daf43 · inbound

Dense Local Dependencies Induce Attention-Logit Explosion and Training Instability During Long-Sequence Transformer Training cites this paper.

Dense Local Dependencies Induce Attention-Logit Explosion and Training Instability During Long-Sequence Transformer Training The Stability-Efficiency Dilemma: Investigating Sequence Length Warmup for Training GPT Models

Reference 19

Resolution
unresolved
no resolver link, observed 2026-08-07T15:19:26.765813Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T15:19:26.765813Z digest=sha256:0335df28fd6bd059f70c1b75c1eae2586073cbc20082254d3b78e0e9141c8c15