Paper Citation Record · LEDGER

Reuse, Don't Retrain: A Recipe for Continued Pretraining of Language Models

As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 19 inbound Pith citation observations for arXiv:2407.07263.

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

pith.paper-citation-record.v1
2407.07263 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 19 of 19 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 19 of 19 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T13:25:04.031066Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-03T16:38:40.742383Z

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 fd1ed86b-332b-4dbe-88af-d0a094d5f3f3 · inbound

WildFeedback: Aligning LLMs With In-situ User Interactions And Feedback cites this paper.

WildFeedback: Aligning LLMs With In-situ User Interactions And Feedback Reuse, Don't Retrain: A Recipe for Continued Pretraining of Language Models

Reference 29

Resolution: verified exact; arxiv_id, observed 2026-05-23T22:38:32.567179Z

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.

Observation 0ba8aaa7-da0c-4ab4-81cd-3341c05d465c · inbound

Optimization Hyper-parameter Laws for Large Language Models cites this paper.

Optimization Hyper-parameter Laws for Large Language Models Reuse, Don't Retrain: A Recipe for Continued Pretraining of Language Models

Reference 30

Resolution: verified exact; arxiv_id, observed 2026-05-23T20:45:48.911815Z

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.

Observation b98ded42-805c-4d71-adf5-217622310fc2 · inbound

Improving Continual Pre-training Through Seamless Data Packing cites this paper.

Improving Continual Pre-training Through Seamless Data Packing Reuse, Don't Retrain: A Recipe for Continued Pretraining of Language Models

Reference 41

Resolution: unresolved; no resolver link, observed 2026-08-07T13:25:04.031066Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 93099af7-621f-4c4d-adcb-63637862ad55 · inbound

Scaling Fine-Grained MoE Beyond 50B Parameters: Empirical Evaluation and Practical Insights cites this paper.

Scaling Fine-Grained MoE Beyond 50B Parameters: Empirical Evaluation and Practical Insights Reuse, Don't Retrain: A Recipe for Continued Pretraining of Language Models

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-07T11:19:00.551984Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0ba2a79d-b21d-4a9d-89a8-fe423663e858 · inbound

The Future of Continual Learning in the Era of Foundation Models: Three Key Directions cites this paper.

The Future of Continual Learning in the Era of Foundation Models: Three Key Directions Reuse, Don't Retrain: A Recipe for Continued Pretraining of Language Models

Reference 58

Resolution: unresolved; no resolver link, observed 2026-08-07T11:10:13.994336Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 131f8fd6-9ce0-4c47-9148-a405da58db28 · inbound

Minifinetuning: Low-Data Generation Domain Adaptation through Corrective Self-Distillation cites this paper.

Minifinetuning: Low-Data Generation Domain Adaptation through Corrective Self-Distillation Reuse, Don't Retrain: A Recipe for Continued Pretraining of Language Models

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-07T12:40:22.685924Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1d71aaed-b70c-4a6c-aaeb-70cc9479450e · inbound

Large-Scale Diverse Synthesis for Mid-Training cites this paper.

Large-Scale Diverse Synthesis for Mid-Training Reuse, Don't Retrain: A Recipe for Continued Pretraining of Language Models

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-06T05:43:17.970063Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c7a52199-8fd9-471e-914d-1d556807ffc8 · inbound

DIET-CP: Lightweight and Data Efficient Self Supervised Continued Pretraining cites this paper.

DIET-CP: Lightweight and Data Efficient Self Supervised Continued Pretraining Reuse, Don't Retrain: A Recipe for Continued Pretraining of Language Models

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-05T11:31:41.909990Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a4c94e9c-f7c0-45bd-8a8a-645b996a1cf1 · inbound

Expert Upcycling: Shifting the Compute-Efficient Frontier of Mixture-of-Experts cites this paper.

Expert Upcycling: Shifting the Compute-Efficient Frontier of Mixture-of-Experts Reuse, Don't Retrain: A Recipe for Continued Pretraining of Language Models

Reference 43

Resolution: verified exact; arxiv_id, observed 2026-05-10T03:29:21.462495Z

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.

Observation 7f42bf97-4b3a-41e1-ac32-5d09c5552202 · inbound

Expert Upcycling: Shifting the Compute-Efficient Frontier of Mixture-of-Experts cites this paper.

Expert Upcycling: Shifting the Compute-Efficient Frontier of Mixture-of-Experts Reuse, Don't Retrain: A Recipe for Continued Pretraining of Language Models

Reference 43

Resolution: verified exact; arxiv_id, observed 2026-05-12T02:06:15.365161Z

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.

Observation 10a7273f-4d27-4471-8063-6ce3f6d60edd · inbound

Phoenix-VL 1.5 Medium Technical Report cites this paper.

Phoenix-VL 1.5 Medium Technical Report Reuse, Don't Retrain: A Recipe for Continued Pretraining of Language Models

Reference 18

Resolution: verified exact; arxiv_id, observed 2026-05-12T06:01:24.857476Z

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.

Observation ad00c35f-7849-4208-b8d5-f4d71724fecb · inbound

Threat Modelling using Domain-Adapted Language Models: Empirical Evaluation and Insights cites this paper.

Threat Modelling using Domain-Adapted Language Models: Empirical Evaluation and Insights Reuse, Don't Retrain: A Recipe for Continued Pretraining of Language Models

Reference 16

Resolution: verified exact; arxiv_id, observed 2026-05-12T06:56:25.794869Z

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.

Observation 5bbe7838-29fb-449c-aaf7-9b56bf363e06 · inbound

Predictable Scaling Laws of Optimal Hyperparameters for LLM Continued Pre-training cites this paper.

Predictable Scaling Laws of Optimal Hyperparameters for LLM Continued Pre-training Reuse, Don't Retrain: A Recipe for Continued Pretraining of Language Models

Reference 14

Resolution: metadata mismatch; arxiv_id, observed 2026-07-02T12:46:56.736284Z

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.

Observation 5c5c1e6b-c84c-4ac8-91f0-7fbdeba31379 · inbound

Small LLMs: Pruning vs. Training from Scratch cites this paper.

Small LLMs: Pruning vs. Training from Scratch Reuse, Don't Retrain: A Recipe for Continued Pretraining of Language Models

Reference 7

Resolution: verified exact; arxiv_id, observed 2026-07-03T16:38:40.744289Z

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.

Observation bfdc60ac-8e71-4bb7-a62c-d4919184a4de · inbound

Small LLMs: Pruning vs. Training from Scratch cites this paper.

Small LLMs: Pruning vs. Training from Scratch Reuse, Don't Retrain: A Recipe for Continued Pretraining of Language Models

Reference 7

Resolution: verified exact; arxiv_id, observed 2026-06-30T11:04:37.582107Z

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.

Observation 18fcd226-fa99-4f15-8010-3f13a9de0adf · inbound

How Post-Training Shapes Biological Reasoning Models cites this paper.

How Post-Training Shapes Biological Reasoning Models Reuse, Don't Retrain: A Recipe for Continued Pretraining of Language Models

Reference 66

Resolution: verified exact; arxiv_id, observed 2026-07-01T07:55:31.048963Z

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.

Observation 5fb69b54-f28c-4bea-95ec-b9069f16e924 · inbound

Train Smarter, Not Longer: Memorization-Guided Data Reuse for Efficient LLM Training cites this paper.

Train Smarter, Not Longer: Memorization-Guided Data Reuse for Efficient LLM Training Reuse, Don't Retrain: A Recipe for Continued Pretraining of Language Models

Reference 12

Resolution: unresolved; no resolver link, observed 2026-07-11T10:45:46.618668Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 82b592df-f960-4faa-aa55-54c321cbcdb7 · inbound

WSqD: A Horizon-Free Learning Rate Schedule for Large Model Training cites this paper.

WSqD: A Horizon-Free Learning Rate Schedule for Large Model Training Reuse, Don't Retrain: A Recipe for Continued Pretraining of Language Models

Reference 47

Resolution: unresolved; no resolver link, observed 2026-07-14T08:04:06.432613Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8a46a709-477f-4666-aea3-e7bf095daf4b · inbound

Scaling Point-in-Time Language Models cites this paper.

Scaling Point-in-Time Language Models Reuse, Don't Retrain: A Recipe for Continued Pretraining of Language Models

Reference 2022

Resolution: unresolved; no resolver link, observed 2026-08-02T15:39:38.991398Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.