Paper Citation Record · LEDGER

Dynamic Loss-Based Sample Reweighting for Improved Large Language Model Pretraining

As of 9 August 2026, this Paper Citation Record lists 48 of 48 outbound references and 1 inbound Pith citation observation for arXiv:2502.06733.

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

pith.paper-citation-record.v1
2502.06733 v1

Coverage vector

measured 48 of 48 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-08T14:36:36.594376Z

measured 49 of 49 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 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T14:09:50.090492Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-07T14:09:51.295780Z

Reference resolution

48 of 48 outbound references displayed

verified exact4
verified fuzzy13
unresolved31
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 64c56565-2fec-4d9c-9e1a-8cea83f807e5 · outbound

This paper cites write newline.

Dynamic Loss-Based Sample Reweighting for Improved Large Language Model Pretraining write newline

Reference 1

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Source-reported events for the cited work

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Observation 145c6055-4bcf-4d83-b6ee-c758d2e238b9 · outbound

This paper cites GPT-4 Technical Report.

Dynamic Loss-Based Sample Reweighting for Improved Large Language Model Pretraining GPT-4 Technical Report

Reference 2

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Source-reported events for the cited work

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Observation b200ef2d-00e0-475e-af3e-b5dc128e5c58 · outbound

This paper cites Piqa: Reasoning about physical commonsense in natural language.

Dynamic Loss-Based Sample Reweighting for Improved Large Language Model Pretraining Piqa: Reasoning about physical commonsense in natural language

Reference 3

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Observation 7a369ffc-4bd7-4a41-82e9-40ba5d586606 · outbound

This paper cites Language models are few-shot learners.

Dynamic Loss-Based Sample Reweighting for Improved Large Language Model Pretraining Language models are few-shot learners

Reference 4

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Observation 037c0e44-3ff5-4e1a-8e51-fc7a9e100b6e · outbound

This paper cites Skill-it! a data-driven skills framework for understanding and training language models.

Dynamic Loss-Based Sample Reweighting for Improved Large Language Model Pretraining Skill-it! a data-driven skills framework for understanding and training language models

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T14:36:37.575749Z

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Observation 9311650d-fc56-431a-ad92-bf9903a64d29 · outbound

This paper cites Take the Bull by the Horns: Hard Sample-Reweighted Continual Training Improves LLM Generalization.

Dynamic Loss-Based Sample Reweighting for Improved Large Language Model Pretraining Take the Bull by the Horns: Hard Sample-Reweighted Continual Training Improves LLM Generalization

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-08T14:36:36.397638Z

Source-reported events for the cited work

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Observation 4053a7a8-7ef9-44c9-9130-7e24b0bcd021 · outbound

This paper cites Palm: Scaling language modeling with pathways.

Dynamic Loss-Based Sample Reweighting for Improved Large Language Model Pretraining Palm: Scaling language modeling with pathways

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-08T14:36:36.402751Z

Source-reported events for the cited work

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Observation 32bddf59-41d5-4333-b82c-361002398ebb · outbound

This paper cites A Farewell to the Bias-Variance Tradeoff? An Overview of the Theory of Overparameterized Machine Learning.

Dynamic Loss-Based Sample Reweighting for Improved Large Language Model Pretraining A Farewell to the Bias-Variance Tradeoff? An Overview of the Theory of Overparameterized Machine Learning

Reference 8

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Source-reported events for the cited work

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Observation d8b248d9-d51d-4735-a035-09d1ebc902fc · outbound

This paper cites The Llama 3 Herd of Models.

Dynamic Loss-Based Sample Reweighting for Improved Large Language Model Pretraining The Llama 3 Herd of Models

Reference 9

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Source-reported events for the cited work

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Observation f164f619-804d-4e77-b050-cb185a0ffaa6 · outbound

This paper cites Irreducible Curriculum for Language Model Pretraining.

Dynamic Loss-Based Sample Reweighting for Improved Large Language Model Pretraining Irreducible Curriculum for Language Model Pretraining

Reference 10

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Source-reported events for the cited work

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Observation 7f18b473-c5e8-4fcf-9a23-c12d131e717c · outbound

This paper cites DOGE : Domain reweighting with generalization estimation.

Dynamic Loss-Based Sample Reweighting for Improved Large Language Model Pretraining DOGE : Domain reweighting with generalization estimation

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T14:36:37.479585Z

Source-reported events for the cited work

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Observation dc59a251-160d-4d96-b914-4ccd88e57a78 · outbound

This paper cites Rethinking importance weighting for deep learning under distribution shift.

Dynamic Loss-Based Sample Reweighting for Improved Large Language Model Pretraining Rethinking importance weighting for deep learning under distribution shift

Reference 12

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Source-reported events for the cited work

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Observation cd800386-1655-4ed8-b3d9-ed3703bc65d7 · outbound

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

Dynamic Loss-Based Sample Reweighting for Improved Large Language Model Pretraining The Pile: An 800GB Dataset of Diverse Text for Language Modeling

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-08T14:36:36.433253Z

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Observation 7351fd65-d779-4697-ba4c-ce4641107993 · outbound

This paper cites Adaptive Training Distributions with Scalable Online Bilevel Optimization.

Dynamic Loss-Based Sample Reweighting for Improved Large Language Model Pretraining Adaptive Training Distributions with Scalable Online Bilevel Optimization

Reference 14

Resolution: verified exact; local_arxiv, observed 2026-08-08T14:36:36.972736Z

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation a60e61d2-3a92-46d6-910b-d1bdddf903c2 · outbound

This paper cites Fedexp: Speeding up federated averaging via extrapolation.

Dynamic Loss-Based Sample Reweighting for Improved Large Language Model Pretraining Fedexp: Speeding up federated averaging via extrapolation

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T14:36:37.394485Z

Source-reported events for the cited work

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Observation a908a46f-4f5f-44e6-9726-cf66d8072390 · outbound

This paper cites Accelerating Deep Learning by Focusing on the Biggest Losers.

Dynamic Loss-Based Sample Reweighting for Improved Large Language Model Pretraining Accelerating Deep Learning by Focusing on the Biggest Losers

Reference 16

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Source-reported events for the cited work

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Observation 6c359732-9b22-4939-a8e4-d99ef841ffa8 · outbound

This paper cites Importance Weighting Can Help Large Language Models Self-Improve.

Dynamic Loss-Based Sample Reweighting for Improved Large Language Model Pretraining Importance Weighting Can Help Large Language Models Self-Improve

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-08T14:36:36.452596Z

Source-reported events for the cited work

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Observation 07d8b422-8c1e-4fc3-88e9-0fcd14d12602 · outbound

This paper cites Instance weighting for domain adaptation in NLP.

Dynamic Loss-Based Sample Reweighting for Improved Large Language Model Pretraining Instance weighting for domain adaptation in NLP

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T14:36:37.315830Z

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 7734a31c-4f42-4cd9-b793-740ca928c358 · outbound

This paper cites Not all samples are created equal: Deep learning with importance sampling.

Dynamic Loss-Based Sample Reweighting for Improved Large Language Model Pretraining Not all samples are created equal: Deep learning with importance sampling

Reference 19

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Observation 2387da00-b5a9-424e-9a12-cb218ebc7a90 · outbound

This paper cites Stochastic Re-weighted Gradient Descent via Distributionally Robust Optimization.

Dynamic Loss-Based Sample Reweighting for Improved Large Language Model Pretraining Stochastic Re-weighted Gradient Descent via Distributionally Robust Optimization

Reference 20

Resolution: verified exact; local_arxiv, observed 2026-08-08T14:36:36.920322Z

Source-reported events for the cited work

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Observation a3df622a-a821-48a8-9992-8b8a25502890 · outbound

This paper cites Probabilistic margins for instance reweighting in adversarial training.

Dynamic Loss-Based Sample Reweighting for Improved Large Language Model Pretraining Probabilistic margins for instance reweighting in adversarial training

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T14:36:37.269915Z

Source-reported events for the cited work

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Observation 5f15e577-aaa0-48e7-8940-52f416cb07cd · outbound

This paper cites Logiqa 2.0—an improved dataset for logical reasoning in natural language understanding.

Dynamic Loss-Based Sample Reweighting for Improved Large Language Model Pretraining Logiqa 2.0—an improved dataset for logical reasoning in natural language understanding

Reference 22

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Observation fa7f26f8-24d7-4ea3-bf6b-19e8538d0901 · outbound

This paper cites LogiQA: A Challenge Dataset for Machine Reading Comprehension with Logical Reasoning.

Dynamic Loss-Based Sample Reweighting for Improved Large Language Model Pretraining LogiQA: A Challenge Dataset for Machine Reading Comprehension with Logical Reasoning

Reference 23

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Observation 3516fb05-27ec-4f9e-b457-255a998579dd · outbound

This paper cites A Pretrainer's Guide to Training Data: Measuring the Effects of Data Age, Domain Coverage, Quality, & Toxicity.

Dynamic Loss-Based Sample Reweighting for Improved Large Language Model Pretraining A Pretrainer's Guide to Training Data: Measuring the Effects of Data Age, Domain Coverage, Quality, & Toxicity

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-08T14:36:36.485151Z

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Observation 49dd77b7-6a50-44d1-8ab5-0364242ec0d8 · outbound

This paper cites Online Batch Selection for Faster Training of Neural Networks.

Dynamic Loss-Based Sample Reweighting for Improved Large Language Model Pretraining Online Batch Selection for Faster Training of Neural Networks

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-08T14:36:36.489866Z

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Observation bc97de34-1176-4300-aac6-3827a5853cb0 · outbound

This paper cites The RefinedWeb Dataset for Falcon LLM: Outperforming Curated Corpora with Web Data, and Web Data Only.

Dynamic Loss-Based Sample Reweighting for Improved Large Language Model Pretraining The RefinedWeb Dataset for Falcon LLM: Outperforming Curated Corpora with Web Data, and Web Data Only

Reference 26

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Observation 9ffc1f42-882c-4357-b311-0b4d9beea84b · outbound

This paper cites The FineWeb Datasets: Decanting the Web for the Finest Text Data at Scale.

Dynamic Loss-Based Sample Reweighting for Improved Large Language Model Pretraining The FineWeb Datasets: Decanting the Web for the Finest Text Data at Scale

Reference 27

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Observation 975a3e06-4a62-4cfd-b667-09b58438ee5f · outbound

This paper cites An online method for a class of distributionally robust optimization with non-convex objectives.

Dynamic Loss-Based Sample Reweighting for Improved Large Language Model Pretraining An online method for a class of distributionally robust optimization with non-convex objectives

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T14:36:37.244956Z

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Observation 878d2deb-6e38-4f9b-852b-67819459e719 · outbound

This paper cites Robust optimization over multiple domains.

Dynamic Loss-Based Sample Reweighting for Improved Large Language Model Pretraining Robust optimization over multiple domains

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T14:36:37.229574Z

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Observation c1fefab7-baa5-48d1-b45e-86375429c6e8 · outbound

This paper cites Language models are unsupervised multitask learners.

Dynamic Loss-Based Sample Reweighting for Improved Large Language Model Pretraining Language models are unsupervised multitask learners

Reference 30

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Observation 65025dcb-73ea-4396-90f3-70901d96ec03 · outbound

This paper cites Overparameterized neural networks implement associative memory.

Dynamic Loss-Based Sample Reweighting for Improved Large Language Model Pretraining Overparameterized neural networks implement associative memory

Reference 31

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Observation d42889cc-31d8-4cbb-a88a-abef68ed65e8 · outbound

This paper cites Exploring the limits of transfer learning with a unified text-to-text transformer.

Dynamic Loss-Based Sample Reweighting for Improved Large Language Model Pretraining Exploring the limits of transfer learning with a unified text-to-text transformer

Reference 32

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Observation c5ae3537-a782-48c7-bfaf-3c5036a19fd4 · outbound

This paper cites Learning to reweight examples for robust deep learning.

Dynamic Loss-Based Sample Reweighting for Improved Large Language Model Pretraining Learning to reweight examples for robust deep learning

Reference 33

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Observation 13e9e73f-5f13-4786-ad4c-071a30b06062 · outbound

This paper cites Almost sure convergence rates for stochastic gradient descent and stochastic heavy ball.

Dynamic Loss-Based Sample Reweighting for Improved Large Language Model Pretraining Almost sure convergence rates for stochastic gradient descent and stochastic heavy ball

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T14:36:37.171130Z

Source-reported events for the cited work

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Observation e0419639-5862-4bba-b4f4-6718335ada6e · outbound

This paper cites SlimPajama: A 627B token cleaned and deduplicated version of RedPajama.

Dynamic Loss-Based Sample Reweighting for Improved Large Language Model Pretraining SlimPajama: A 627B token cleaned and deduplicated version of RedPajama

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T14:36:37.155346Z

Source-reported events for the cited work

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Observation 1b499208-2bbf-46ad-92e4-da3ff31f1400 · outbound

This paper cites Doubly Robust Instance-Reweighted Adversarial Training.

Dynamic Loss-Based Sample Reweighting for Improved Large Language Model Pretraining Doubly Robust Instance-Reweighted Adversarial Training

Reference 36

Resolution: verified exact; local_arxiv, observed 2026-08-08T14:36:36.823339Z

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Observation 067775a3-e8a9-4d8c-97a7-8b0dc06906b9 · outbound

This paper cites Self-Influence Guided Data Reweighting for Language Model Pre-training.

Dynamic Loss-Based Sample Reweighting for Improved Large Language Model Pretraining Self-Influence Guided Data Reweighting for Language Model Pre-training

Reference 37

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Observation 91fc5ff7-dcfe-4697-8dfd-916f31101db7 · outbound

This paper cites Llama 2: Open Foundation and Fine-Tuned Chat Models.

Dynamic Loss-Based Sample Reweighting for Improved Large Language Model Pretraining Llama 2: Open Foundation and Fine-Tuned Chat Models

Reference 38

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Observation e40a2f10-2db2-4ca9-ae72-dd7e5256a23d · outbound

This paper cites Attention is all you need.

Dynamic Loss-Based Sample Reweighting for Improved Large Language Model Pretraining Attention is all you need

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-08T14:36:36.553280Z

Source-reported events for the cited work

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Observation 14980b0b-66f3-4c25-b84e-ab3672c9b73e · outbound

This paper cites Liu, and Matt Gardner.

Dynamic Loss-Based Sample Reweighting for Improved Large Language Model Pretraining Liu, and Matt Gardner

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T14:36:37.130683Z

Source-reported events for the cited work

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Observation 6e524103-9ed9-4afc-8b46-379e36ffe1f5 · outbound

This paper cites Q u R ating: Selecting high-quality data for training language models.

Dynamic Loss-Based Sample Reweighting for Improved Large Language Model Pretraining Q u R ating: Selecting high-quality data for training language models

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T14:36:37.115261Z

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 f0addaf2-1bbc-44bb-a1ac-08568136d1bd · outbound

This paper cites DoReMi: Optimizing Data Mixtures Speeds Up Language Model Pretraining.

Dynamic Loss-Based Sample Reweighting for Improved Large Language Model Pretraining DoReMi: Optimizing Data Mixtures Speeds Up Language Model Pretraining

Reference 42

Resolution: unresolved; no resolver link, observed 2026-08-08T14:36:36.566504Z

Source-reported events for the cited work

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Observation 292adc5b-079c-420b-9b21-1ad503d3cbc8 · outbound

This paper cites Reweighting Augmented Samples by Minimizing the Maximal Expected Loss.

Dynamic Loss-Based Sample Reweighting for Improved Large Language Model Pretraining Reweighting Augmented Samples by Minimizing the Maximal Expected Loss

Reference 43

Resolution: verified exact; local_arxiv, observed 2026-08-08T14:36:36.755381Z

Source-reported events for the cited work

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Observation 0d86ab4b-182f-4ed0-b62d-bbcbe860286c · outbound

This paper cites Are adversarial examples created equal? a learnable weighted minimax risk for robustness under non-uniform attacks.

Dynamic Loss-Based Sample Reweighting for Improved Large Language Model Pretraining Are adversarial examples created equal? a learnable weighted minimax risk for robustness under non-uniform attacks

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T14:36:37.100186Z

Source-reported events for the cited work

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Observation a5493920-4563-4eb5-95f9-ec3f4c696e37 · outbound

This paper cites Geometry-aware Instance-reweighted Adversarial Training.

Dynamic Loss-Based Sample Reweighting for Improved Large Language Model Pretraining Geometry-aware Instance-reweighted Adversarial Training

Reference 45

Resolution: unresolved; no resolver link, observed 2026-08-08T14:36:36.580744Z

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Observation 2b7bc779-e2e8-42af-8d5c-9d3a65f6e1c7 · outbound

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Dynamic Loss-Based Sample Reweighting for Improved Large Language Model Pretraining @esa (Ref

Reference 46

Resolution: unresolved; no resolver link, observed 2026-08-08T14:36:36.585174Z

Source-reported events for the cited work

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Observation 0b37c6c4-73d3-4613-b482-42a83b623b7c · outbound

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Dynamic Loss-Based Sample Reweighting for Improved Large Language Model Pretraining Unresolved cited work

Reference 47

Resolution: unresolved; no resolver link, observed 2026-08-08T14:36:36.590108Z

Source-reported events for the cited work

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Observation fecb48e0-38df-4660-bc77-e3154feb128c · outbound

This paper cites 誁ڞYBq0 w @ \˂bF 3`^) f ` WޏTb]tQ Z Ы;]2Zj8ݐlW c kX֏'y S! QfĊ GYs) W 4 P0,Or (W ; )C NƢ). ; W `m GN 徐ݏիhF ސWW xu3ur]!54#VO=? ±* ^p rx ]K f hFIf QY b g+ <mcOt3Gܘ tX.

Dynamic Loss-Based Sample Reweighting for Improved Large Language Model Pretraining 誁ڞYBq0 w @ \˂bF 3`^) f ` WޏTb]tQ Z Ы;]2Zj8ݐlW c kX֏'y S! QfĊ GYs) W 4 P0,Or (W ; )C NƢ). ; W `m GN 徐ݏիhF ސWW xu3ur]!54#VO=? ±* ^p rx ]K f hFIf QY b g+ <mcOt3Gܘ tX

Reference 48

Resolution: unresolved; no resolver link, observed 2026-08-08T14:36:36.594376Z

Source-reported events for the cited work

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Pith citing papers

Observation 457b51ea-2390-4ed0-8f05-fefe992ccef0 · inbound

ESLM: Risk-Averse Selective Language Modeling for Efficient Pretraining cites this paper.

ESLM: Risk-Averse Selective Language Modeling for Efficient Pretraining Dynamic Loss-Based Sample Reweighting for Improved Large Language Model Pretraining

Reference 50

Resolution: verified exact; local_arxiv, observed 2026-08-07T14:09:51.406696Z

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.