Paper Citation Record · LEDGER

Scaling Law with Learning Rate Annealing

As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:2408.11029.

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

pith.paper-citation-record.v1
2408.11029 v2

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 2026-08-09T06:31:02.800959+00:00

measured 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-08T17:01:36.194110Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-06-30T08:24:26.792264Z

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 db0581da-92ba-4af8-90c0-2553e2f59445 · inbound

Scaling Laws for Forgetting during Finetuning with Pretraining Data Injection cites this paper.

Scaling Laws for Forgetting during Finetuning with Pretraining Data Injection Scaling Law with Learning Rate Annealing

Reference 41

Resolution: unresolved; no resolver link, observed 2026-08-08T17:01:36.194110Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8b1f55d0-2325-4943-aadd-c42eb35e4330 · inbound

Training Dynamics Underlying Language Model Scaling Laws: Loss Deceleration and Zero-Sum Learning cites this paper.

Training Dynamics Underlying Language Model Scaling Laws: Loss Deceleration and Zero-Sum Learning Scaling Law with Learning Rate Annealing

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-07T10:31:20.522740Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0d53d82b-059b-4a33-be5a-6c88d9f6f97d · inbound

Scaling Collapse Reveals Universal Dynamics in Compute-Optimally Trained Neural Networks cites this paper.

Scaling Collapse Reveals Universal Dynamics in Compute-Optimally Trained Neural Networks Scaling Law with Learning Rate Annealing

Reference 44

Resolution: unresolved; no resolver link, observed 2026-08-06T20:48:54.159538Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 068cd734-0431-4fcc-8a9b-977367f48ca5 · inbound

On the Nonlinearity of Learning Rate Scaling for LLM Training cites this paper.

On the Nonlinearity of Learning Rate Scaling for LLM Training Scaling Law with Learning Rate Annealing

Reference 36

Resolution: verified exact; arxiv_id, observed 2026-06-30T08:24:26.793968Z

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.