Paper Citation Record · LEDGER

Improving the Scaling Laws of Synthetic Data with Deliberate Practice

As of 23 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 7 inbound Pith citation observations for arXiv:2502.15588.

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

pith.paper-citation-record.v1
2502.15588 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 7 of 7 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-23T06:30:58.430688+00:00

measured 7 of 7 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T23:22:05.899139Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-14T20:52:58.604835Z

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 9b676711-8ae3-4efe-961f-ba2d8cb2068e · inbound

Boosting Statistic Learning with Synthetic Data from Pretrained Large Models cites this paper.

Boosting Statistic Learning with Synthetic Data from Pretrained Large Models Improving the Scaling Laws of Synthetic Data with Deliberate Practice

Reference 75

Resolution: unresolved; no resolver link, observed 2026-08-15T23:22:05.899139Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bc33e06f-5803-4cba-8572-96046db87081 · inbound

The Anti-Ouroboros Effect: Emergent Resilience in Large Language Models from Recursive Selective Feedback cites this paper.

The Anti-Ouroboros Effect: Emergent Resilience in Large Language Models from Recursive Selective Feedback Improving the Scaling Laws of Synthetic Data with Deliberate Practice

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-05T12:04:03.747907Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 77b96fac-7e26-4eb1-ac15-f49e139c646d · inbound

MARS: Margin and Semantic-Aware Data Augmentation for Reward Modeling cites this paper.

MARS: Margin and Semantic-Aware Data Augmentation for Reward Modeling Improving the Scaling Laws of Synthetic Data with Deliberate Practice

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-02T22:14:31.082865Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e92a7ed5-38fb-4c24-afbd-dad4228d922d · inbound

LiBaGS: Lightweight Boundary Gap Synthesis for Targeted Synthetic Data Selection cites this paper.

LiBaGS: Lightweight Boundary Gap Synthesis for Targeted Synthetic Data Selection Improving the Scaling Laws of Synthetic Data with Deliberate Practice

Reference 2

Resolution: verified exact; arxiv_id, observed 2026-05-13T02:32:06.356463Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation e2bb2d07-a902-4041-a789-114619c104b0 · inbound

LiBaGS: Lightweight Boundary Gap Synthesis for Targeted Synthetic Data Selection cites this paper.

LiBaGS: Lightweight Boundary Gap Synthesis for Targeted Synthetic Data Selection Improving the Scaling Laws of Synthetic Data with Deliberate Practice

Reference 2

Resolution: verified exact; arxiv_id, observed 2026-05-14T20:52:58.608381Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 3ce1a748-ad7a-4d77-af51-14c28d496a84 · inbound

Influence Diagnostics in High-dimensional M-estimation: Precise Asymptotics cites this paper.

Influence Diagnostics in High-dimensional M-estimation: Precise Asymptotics Improving the Scaling Laws of Synthetic Data with Deliberate Practice

Reference 64

Resolution: unresolved; no resolver link, observed 2026-07-13T04:24:43.867063Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7eb113d8-e4f9-47c1-9824-e2c475a4fecb · inbound

Bridging Compute- and Data-Optimal Pretraining cites this paper.

Bridging Compute- and Data-Optimal Pretraining Improving the Scaling Laws of Synthetic Data with Deliberate Practice

Reference 54

Resolution: unresolved; no resolver link, observed 2026-08-01T03:02:02.342443Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.