Paper Citation Record · LEDGER

Scaling Laws for Forgetting during Finetuning with Pretraining Data Injection

As of 14 August 2026, this Paper Citation Record lists 48 of 48 outbound references and 7 inbound Pith citation observations for arXiv:2502.06042.

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

pith.paper-citation-record.v1
2502.06042 v2

Coverage vector

measured 48 of 48 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-08T17:01:36.218426Z

measured 55 of 55 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-13T06:32:02.005865+00:00

measured 7 of 7 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-01T22:30:58.664303Z

measured 1 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Reference resolution

48 of 48 outbound references displayed

verified exact2
verified fuzzy13
unresolved33
parse uncertain0
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External citation measurements

0
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

Observation 0e539dbd-18d5-4e79-8178-391069d27b79 · outbound

This paper cites Scaling laws for generative mixed-modal language models.

Scaling Laws for Forgetting during Finetuning with Pretraining Data Injection Scaling laws for generative mixed-modal language models

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T17:01:36.874266Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 54015f12-73e8-4298-bc75-689f38c3d2a1 · outbound

This paper cites Physics in Next-token Prediction.

Scaling Laws for Forgetting during Finetuning with Pretraining Data Injection Physics in Next-token Prediction

Reference 2

Resolution: verified exact; local_arxiv, observed 2026-08-08T17:01:36.675840Z

Source-reported events for the cited work

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Observation 348d53aa-d60c-4d10-bf07-05829460d3d8 · outbound

This paper cites An Empirical Study of Scaling Laws for Transfer.

Scaling Laws for Forgetting during Finetuning with Pretraining Data Injection An Empirical Study of Scaling Laws for Transfer

Reference 3

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Observation 5ad549d4-7fac-4b5a-90c7-53a13cc61fbb · outbound

This paper cites Chinchilla Scaling: A replication attempt.

Scaling Laws for Forgetting during Finetuning with Pretraining Data Injection Chinchilla Scaling: A replication attempt

Reference 4

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Observation d3d0fa82-5ef3-490b-8ea0-13d01b547b18 · outbound

This paper cites Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge.

Scaling Laws for Forgetting during Finetuning with Pretraining Data Injection Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge

Reference 5

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Observation f5824e7e-4bcb-4e98-be0e-ebfb8295506d · outbound

This paper cites DeepSeek LLM: Scaling Open-Source Language Models with Longtermism.

Scaling Laws for Forgetting during Finetuning with Pretraining Data Injection DeepSeek LLM: Scaling Open-Source Language Models with Longtermism

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-08T17:01:36.066170Z

Source-reported events for the cited work

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Observation 6f0003f1-0c81-4ad5-8ba1-00c8381cdae5 · outbound

This paper cites The Pile: An 800GB Dataset of Diverse Text for Language Modeling.

Scaling Laws for Forgetting during Finetuning with Pretraining Data Injection The Pile: An 800GB Dataset of Diverse Text for Language Modeling

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-08T17:01:36.070822Z

Source-reported events for the cited work

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Observation 12aa82ea-4964-4565-8705-4c5e29328f9b · outbound

This paper cites B., Werra, L.

Scaling Laws for Forgetting during Finetuning with Pretraining Data Injection B., Werra, L

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T17:01:36.864014Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation dd78c85f-f506-471a-ab91-a72768059e65 · outbound

This paper cites The elements of statistical learning: data mining, inference, and prediction, 2017.

Scaling Laws for Forgetting during Finetuning with Pretraining Data Injection The elements of statistical learning: data mining, inference, and prediction, 2017

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T17:01:36.854328Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 2bf46535-cc36-49d7-abf5-1af770924f88 · outbound

This paper cites Towards a Unified View of Parameter-Efficient Transfer Learning.

Scaling Laws for Forgetting during Finetuning with Pretraining Data Injection Towards a Unified View of Parameter-Efficient Transfer Learning

Reference 10

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Source-reported events for the cited work

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Observation df1de12e-323e-4169-a5d7-b9b9546a62ed · outbound

This paper cites Measuring massive multitask language understanding.

Scaling Laws for Forgetting during Finetuning with Pretraining Data Injection Measuring massive multitask language understanding

Reference 11

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Observation 722dd3be-544e-49c2-891c-b678f9a43b81 · outbound

This paper cites Scaling Laws for Transfer.

Scaling Laws for Forgetting during Finetuning with Pretraining Data Injection Scaling Laws for Transfer

Reference 12

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Source-reported events for the cited work

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Observation 19d8cfee-df61-4bc6-8e13-6b387f11ea40 · outbound

This paper cites Deep Learning Scaling is Predictable, Empirically.

Scaling Laws for Forgetting during Finetuning with Pretraining Data Injection Deep Learning Scaling is Predictable, Empirically

Reference 13

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Observation 670b62d3-df1b-43f4-ac17-63c093676837 · outbound

This paper cites Disentangling and Mitigating the Impact of Task Similarity for Continual Learning.

Scaling Laws for Forgetting during Finetuning with Pretraining Data Injection Disentangling and Mitigating the Impact of Task Similarity for Continual Learning

Reference 14

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Observation d3629529-3168-4d20-bb8d-ce8902bde973 · outbound

This paper cites Training Compute-Optimal Large Language Models.

Scaling Laws for Forgetting during Finetuning with Pretraining Data Injection Training Compute-Optimal Large Language Models

Reference 15

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Observation 7ffca25e-5a4d-4446-8e11-50e9128774da · outbound

This paper cites an unresolved cited work.

Scaling Laws for Forgetting during Finetuning with Pretraining Data Injection Unresolved cited work

Reference 16

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Observation 13153a14-8642-4f09-a584-d0d7da39aedc · outbound

This paper cites Parameter-Efficient Transfer Learning for NLP.

Scaling Laws for Forgetting during Finetuning with Pretraining Data Injection Parameter-Efficient Transfer Learning for NLP

Reference 17

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Observation 2f570756-ec8c-491d-9c7f-683279943162 · outbound

This paper cites LoRA: Low-Rank Adaptation of Large Language Models.

Scaling Laws for Forgetting during Finetuning with Pretraining Data Injection LoRA: Low-Rank Adaptation of Large Language Models

Reference 18

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Observation 5e9eebac-00a9-4b75-9358-e17ecb98a7fd · outbound

This paper cites L., Wang, C., Yao, Y., Zhao, C., Zhou, J., Cai, J., Zhai, Z., Ding, N., Jia, C., Zeng, G., dahai li, Liu, Z., and Sun, M.

Scaling Laws for Forgetting during Finetuning with Pretraining Data Injection L., Wang, C., Yao, Y., Zhao, C., Zhou, J., Cai, J., Zhai, Z., Ding, N., Jia, C., Zeng, G., dahai li, Liu, Z., and Sun, M

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T17:01:36.825506Z

Source-reported events for the cited work

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Observation 32c3eda3-14ab-4ce6-820c-2666f8d054b3 · outbound

This paper cites Simple and Scalable Strategies to Continually Pre-train Large Language Models.

Scaling Laws for Forgetting during Finetuning with Pretraining Data Injection Simple and Scalable Strategies to Continually Pre-train Large Language Models

Reference 20

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Observation c1c71820-692a-4f1a-a23e-6be4f017bc7c · outbound

This paper cites Scaling laws for downstream task performance of large language models.

Scaling Laws for Forgetting during Finetuning with Pretraining Data Injection Scaling laws for downstream task performance of large language models

Reference 21

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Observation 46cd8a44-6f4f-4b39-bed4-e6aae40e8a25 · outbound

This paper cites Scaling Laws for Forgetting When Fine-Tuning Large Language Models.

Scaling Laws for Forgetting during Finetuning with Pretraining Data Injection Scaling Laws for Forgetting When Fine-Tuning Large Language Models

Reference 22

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Observation eb9e3c6f-cab7-4d45-be90-58871f1ca401 · outbound

This paper cites Get more for less: Principled Data Selection for Warming Up Fine-Tuning in LLMs.

Scaling Laws for Forgetting during Finetuning with Pretraining Data Injection Get more for less: Principled Data Selection for Warming Up Fine-Tuning in LLMs

Reference 23

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Observation f69888e6-bb5c-4108-8578-105fac66082d · outbound

This paper cites Scaling Laws for Neural Language Models.

Scaling Laws for Forgetting during Finetuning with Pretraining Data Injection Scaling Laws for Neural Language Models

Reference 24

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Observation d4b50a27-f704-4b8b-b279-6409a8d22899 · outbound

This paper cites SentencePiece: A simple and language independent subword tokenizer and detokenizer for Neural Text Processing.

Scaling Laws for Forgetting during Finetuning with Pretraining Data Injection SentencePiece: A simple and language independent subword tokenizer and detokenizer for Neural Text Processing

Reference 25

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Observation 9e551a4f-ba48-4fff-85fc-cd1a357abb79 · outbound

This paper cites Improved fine-tuning by better leveraging pre-training data.

Scaling Laws for Forgetting during Finetuning with Pretraining Data Injection Improved fine-tuning by better leveraging pre-training data

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T17:01:36.814908Z

Source-reported events for the cited work

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Observation 5bdfefc6-a351-409c-ac89-22579ccc7eac · outbound

This paper cites and Hutter, F.

Scaling Laws for Forgetting during Finetuning with Pretraining Data Injection and Hutter, F

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T17:01:36.803295Z

Source-reported events for the cited work

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Observation 9fa8498c-dad9-43e8-a60c-a0e333f548b3 · outbound

This paper cites and Hutter, F.

Scaling Laws for Forgetting during Finetuning with Pretraining Data Injection and Hutter, F

Reference 28

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Observation b0701b48-8b4b-4e01-86cc-847043c43dbd · outbound

This paper cites An Empirical Study of Catastrophic Forgetting in Large Language Models During Continual Fine-tuning.

Scaling Laws for Forgetting during Finetuning with Pretraining Data Injection An Empirical Study of Catastrophic Forgetting in Large Language Models During Continual Fine-tuning

Reference 29

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Observation 89f4ec0a-15e4-410c-9400-07e39d7e2ef8 · outbound

This paper cites LLMs on the Line: Data Determines Loss-to-Loss Scaling Laws.

Scaling Laws for Forgetting during Finetuning with Pretraining Data Injection LLMs on the Line: Data Determines Loss-to-Loss Scaling Laws

Reference 30

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Observation 22ad3363-1613-43d8-a6f3-8698700c4d9a · outbound

This paper cites Metaicl: Learning to learn in context.

Scaling Laws for Forgetting during Finetuning with Pretraining Data Injection Metaicl: Learning to learn in context

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T17:01:36.786124Z

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Observation b42c65b6-0a14-4b5f-a976-f198e5fb034a · outbound

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Scaling Laws for Forgetting during Finetuning with Pretraining Data Injection Unresolved cited work

Reference 32

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Observation 275cdcbb-c633-49dd-8b0e-b6b879a9b018 · outbound

This paper cites Training language models to follow instructions with human feedback.

Scaling Laws for Forgetting during Finetuning with Pretraining Data Injection Training language models to follow instructions with human feedback

Reference 33

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Observation 774c7d91-55e4-49be-8a31-36fbb86bdd82 · outbound

This paper cites Resolving Discrepancies in Compute-Optimal Scaling of Language Models.

Scaling Laws for Forgetting during Finetuning with Pretraining Data Injection Resolving Discrepancies in Compute-Optimal Scaling of Language Models

Reference 34

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Observation d1acaf38-48d1-4533-b92d-d1ccf2ea4737 · outbound

This paper cites Self-attention Does Not Need $O(n^2)$ Memory.

Scaling Laws for Forgetting during Finetuning with Pretraining Data Injection Self-attention Does Not Need $O(n^2)$ Memory

Reference 35

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Scaling Laws for Forgetting during Finetuning with Pretraining Data Injection Language models are unsupervised multitask learners

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T17:01:36.760912Z

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Observation 9e25533c-d209-44cc-a08c-28141256f423 · outbound

This paper cites Multitask prompted training enables zero-shot task generalization.

Scaling Laws for Forgetting during Finetuning with Pretraining Data Injection Multitask prompted training enables zero-shot task generalization

Reference 37

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Source-reported events for the cited work

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Observation 642d8bb8-aa55-41cd-a887-240b8c4f5928 · outbound

This paper cites Beyond Chinchilla-Optimal: Accounting for Inference in Language Model Scaling Laws.

Scaling Laws for Forgetting during Finetuning with Pretraining Data Injection Beyond Chinchilla-Optimal: Accounting for Inference in Language Model Scaling Laws

Reference 38

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Scaling Laws for Forgetting during Finetuning with Pretraining Data Injection Sequence to Sequence Learning with Neural Networks

Reference 39

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Source-reported events for the cited work

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Observation 3b9816fa-688c-4341-b4a2-73273cece9e3 · outbound

This paper cites Openhermes 2.5: An open dataset of synthetic data for generalist llm assistants, 2023.

Scaling Laws for Forgetting during Finetuning with Pretraining Data Injection Openhermes 2.5: An open dataset of synthetic data for generalist llm assistants, 2023

Reference 40

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Observation db0581da-92ba-4af8-90c0-2553e2f59445 · outbound

This paper cites Scaling Law with Learning Rate Annealing.

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

Reference 41

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Scaling Laws for Forgetting during Finetuning with Pretraining Data Injection When Precision Meets Position: BFloat16 Breaks Down RoPE in Long-Context Training

Reference 42

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Observation e4c8af24-347c-4f88-8d86-0e579484ba57 · outbound

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Scaling Laws for Forgetting during Finetuning with Pretraining Data Injection Unresolved cited work

Reference 43

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Observation 9c8321d0-e366-4e90-b046-338df60caa46 · outbound

This paper cites W., Lester, B., Du, N., Dai, A.

Scaling Laws for Forgetting during Finetuning with Pretraining Data Injection W., Lester, B., Du, N., Dai, A

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T17:01:36.717023Z

Source-reported events for the cited work

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Observation ec7e55ca-b143-495b-bae8-4fbbdeee3771 · outbound

This paper cites What makes a high-quality training dataset for large language models: A practitioners' perspective.

Scaling Laws for Forgetting during Finetuning with Pretraining Data Injection What makes a high-quality training dataset for large language models: A practitioners' perspective

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T17:01:36.705655Z

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Observation 8e58dd0b-5e36-44bd-a36e-76d72e08d32c · outbound

This paper cites When scaling meets LLM finetuning: The effect of data, model and finetuning method.

Scaling Laws for Forgetting during Finetuning with Pretraining Data Injection When scaling meets LLM finetuning: The effect of data, model and finetuning method

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T17:01:36.693831Z

Source-reported events for the cited work

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Observation da611d14-44c9-419b-bc38-a58e6281c029 · outbound

This paper cites Asymmetry in Low-Rank Adapters of Foundation Models.

Scaling Laws for Forgetting during Finetuning with Pretraining Data Injection Asymmetry in Low-Rank Adapters of Foundation Models

Reference 47

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Observation 170695dc-26c0-4cb5-af04-f682e50a23ea · outbound

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Scaling Laws for Forgetting during Finetuning with Pretraining Data Injection write newline

Reference 48

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Pith citing papers

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Diversity in Large Language Models under Supervised Fine-Tuning cites this paper.

Diversity in Large Language Models under Supervised Fine-Tuning Scaling Laws for Forgetting during Finetuning with Pretraining Data Injection

Reference 58

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Observation 978121c7-1d7d-4fd1-beeb-272e51c1da1b · inbound

Diversity in Large Language Models under Supervised Fine-Tuning cites this paper.

Diversity in Large Language Models under Supervised Fine-Tuning Scaling Laws for Forgetting during Finetuning with Pretraining Data Injection

Reference 58

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Source-reported events for the cited work

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Observation 85bd389e-4357-4b8c-aa69-868cf41f28f9 · inbound

Early Data Exposure Improves Robustness to Subsequent Fine-Tuning cites this paper.

Early Data Exposure Improves Robustness to Subsequent Fine-Tuning Scaling Laws for Forgetting during Finetuning with Pretraining Data Injection

Reference 3

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Observation 960bddf1-eedd-4e95-98d1-0465059c9ed9 · inbound

Scaling Laws for Mixture Pretraining Under Data Constraints cites this paper.

Scaling Laws for Mixture Pretraining Under Data Constraints Scaling Laws for Forgetting during Finetuning with Pretraining Data Injection

Reference 9

Resolution: verified exact; arxiv_id, observed 2026-05-14T21:48:00.978072Z

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Observation 0701f0ff-3b37-47eb-9a35-895ffbbcad32 · inbound

Forgetting in Language Models: Capacity, Optimization, and Self-Generated Replay cites this paper.

Forgetting in Language Models: Capacity, Optimization, and Self-Generated Replay Scaling Laws for Forgetting during Finetuning with Pretraining Data Injection

Reference 4

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Observation 0e5085ee-2d3f-4315-a358-1eabbe3127eb · inbound

Knowledge Editing in Masked Diffusion Language Models cites this paper.

Knowledge Editing in Masked Diffusion Language Models Scaling Laws for Forgetting during Finetuning with Pretraining Data Injection

Reference 37

Resolution: verified exact; arxiv_id, observed 2026-07-02T03:36:29.935755Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 85a28b91-5f85-4d9c-af51-c4cff710587a · inbound

A Predict-then-Correct Loop Based on Few-Shot Continuous Contextual Bandit for Demand Forecasting cites this paper.

A Predict-then-Correct Loop Based on Few-Shot Continuous Contextual Bandit for Demand Forecasting Scaling Laws for Forgetting during Finetuning with Pretraining Data Injection

Reference 55

Resolution: unresolved; no resolver link, observed 2026-08-01T22:30:58.664303Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.