Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Train Large, Then Compress: Rethinking Model Size for Efficient Training and Inference of Transformers
As of 6 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2002.11794.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-06T06:34:29.942622+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-05-18T00:58:13.116663Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-05-18T00:58:13.282745Z
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 285851e4-2cd8-4642-921c-64130be57f60 · inbound
Scaling Laws for Autoregressive Generative Modeling cites this paper.
Scaling Laws for Autoregressive Generative Modeling Train Large, Then Compress: Rethinking Model Size for Efficient Training and Inference of Transformers
Reference 14
- Resolution
- verified exact
- arxiv_id, observed 2026-05-13T07:49:43.839011Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation 9cefc9e6-0440-4577-a2c9-b3ffe93a2901 · inbound
Scaling Laws for Transfer cites this paper.
Scaling Laws for Transfer Train Large, Then Compress: Rethinking Model Size for Efficient Training and Inference of Transformers
Reference 43
- Resolution
- verified exact
- arxiv_id, observed 2026-05-18T00:58:13.285137Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation f66a64cd-c2c3-45cd-b812-bc3bcf822ec4 · inbound
A General Language Assistant as a Laboratory for Alignment cites this paper.
A General Language Assistant as a Laboratory for Alignment Train Large, Then Compress: Rethinking Model Size for Efficient Training and Inference of Transformers
Reference 71
- Resolution
- verified exact
- arxiv_id, observed 2026-05-11T14:22:59.520797Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation d36dce0d-ee55-4c0f-8fb9-33899f190b82 · inbound
Language Models (Mostly) Know What They Know cites this paper.
Language Models (Mostly) Know What They Know Train Large, Then Compress: Rethinking Model Size for Efficient Training and Inference of Transformers
Reference 129
- Resolution
- verified exact
- arxiv_id, observed 2026-05-10T15:42:47.765535Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation b448464b-74a8-40b0-8ca0-e15f4d3bdf7c · inbound
Multi-Aspect Knowledge Distillation for Language Model with Low-rank Factorization cites this paper.
Multi-Aspect Knowledge Distillation for Language Model with Low-rank Factorization Train Large, Then Compress: Rethinking Model Size for Efficient Training and Inference of Transformers
Reference 2
- Resolution
- metadata mismatch
- arxiv_id, observed 2026-05-13T20:38:14.644305Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.