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Paper Citation Record · LEDGER

Tune As You Scale: Hyperparameter Optimization For Compute Efficient Training

As of 14 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2306.08055.

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

pith.paper-citation-record.v1
2306.08055 v1

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 2026-08-14T06:32:32.682623+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T18:51:56.011932Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-14T20:02:53.057562Z

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 989f5c67-a6b1-42ec-96bd-ff79ac28022e · inbound

Combining Pre-Trained Models for Enhanced Feature Representation in Reinforcement Learning cites this paper.

Combining Pre-Trained Models for Enhanced Feature Representation in Reinforcement Learning Tune As You Scale: Hyperparameter Optimization For Compute Efficient Training

Reference 14

Resolution
unresolved
no resolver link, observed 2026-08-06T18:51:56.011932Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T18:51:56.011932Z digest=sha256:e7df05626b77da99a4f9a0c42f68a8327198adfeda05712916a757b900667266

Observation 039146de-b5d7-4940-b374-f3a16c2b65b2 · inbound

When is Warmstarting Effective for Scaling Language Models? cites this paper.

When is Warmstarting Effective for Scaling Language Models? Tune As You Scale: Hyperparameter Optimization For Compute Efficient Training

Reference 8

Resolution
verified exact
arxiv_id, observed 2026-05-14T20:02:53.060870Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-05-14T20:00:47.126736Z digest=sha256:659d07279b0edebec2832bb8aef92c156c2a7cc07d07ed1c1fbde78624e37bcb