Paper Citation Record · LEDGER

When Less is More: Investigating Data Pruning for Pretraining LLMs at Scale

As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 35 inbound Pith citation observations for arXiv:2309.04564.

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

pith.paper-citation-record.v1
2309.04564 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 35 of 35 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+00:00

measured 35 of 35 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-09T19:31:49.697043Z

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

0 of 0 outbound references displayed

verified exact0
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External citation measurements

7
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation c894b174-2f1a-4007-92fb-7003f519a3fe · inbound

A Survey of Large Language Models cites this paper.

A Survey of Large Language Models When Less is More: Investigating Data Pruning for Pretraining LLMs at Scale

Reference 236

Resolution: verified exact; arxiv_id, observed 2026-05-10T22:46:40.287347Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 2cabdda0-928c-42f8-8775-028b8a8be8ec · inbound

MetaMath: Bootstrap Your Own Mathematical Questions for Large Language Models cites this paper.

MetaMath: Bootstrap Your Own Mathematical Questions for Large Language Models When Less is More: Investigating Data Pruning for Pretraining LLMs at Scale

Reference 46

Resolution: verified exact; arxiv_id, observed 2026-05-13T10:07:53.863501Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 80cb290a-d72a-4454-980d-5e17b1e77046 · inbound

How Good is Your Wikipedia? Auditing Data Quality for Low-resource and Multilingual NLP cites this paper.

How Good is Your Wikipedia? Auditing Data Quality for Low-resource and Multilingual NLP When Less is More: Investigating Data Pruning for Pretraining LLMs at Scale

Reference 39

Resolution: verified exact; arxiv_id, observed 2026-05-23T17:38:15.898675Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation dc25a264-3f1c-4d21-b0ac-b1506e6ed1bb · inbound

From Few to Many: Self-Improving Many-Shot Reasoners Through Iterative Optimization and Generation cites this paper.

From Few to Many: Self-Improving Many-Shot Reasoners Through Iterative Optimization and Generation When Less is More: Investigating Data Pruning for Pretraining LLMs at Scale

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-09T19:31:49.697043Z

Source-reported events for the cited work

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Observation 9fa03547-68b8-47b7-b02f-0e39ab57bd3c · inbound

Visual Compositional Tuning cites this paper.

Visual Compositional Tuning When Less is More: Investigating Data Pruning for Pretraining LLMs at Scale

Reference 13

Resolution: verified exact; arxiv_id, observed 2026-05-22T17:41:53.142850Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 58768ad4-65f5-4480-8f5d-e1fd7c9863c9 · inbound

Enhancing LLMs via High-Knowledge Data Selection cites this paper.

Enhancing LLMs via High-Knowledge Data Selection When Less is More: Investigating Data Pruning for Pretraining LLMs at Scale

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-07T15:43:02.614781Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d1e39d41-ff38-45d0-ba22-d7320806b40d · inbound

Small-to-Large Generalization: Data Influences Models Consistently Across Scale cites this paper.

Small-to-Large Generalization: Data Influences Models Consistently Across Scale When Less is More: Investigating Data Pruning for Pretraining LLMs at Scale

Reference 2023

Resolution: unresolved; no resolver link, observed 2026-08-07T15:08:26.725536Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9d0b6135-a217-482e-9dbc-88e0d434b1f0 · inbound

Improving Chemical Understanding of LLMs via SMILES Parsing cites this paper.

Improving Chemical Understanding of LLMs via SMILES Parsing When Less is More: Investigating Data Pruning for Pretraining LLMs at Scale

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-07T15:06:45.356862Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7de29b8e-dede-49df-828d-71792f8cc102 · inbound

A Survey of LLM $\times$ DATA cites this paper.

A Survey of LLM $\times$ DATA When Less is More: Investigating Data Pruning for Pretraining LLMs at Scale

Reference 285

Resolution: unresolved; no resolver link, observed 2026-08-07T14:33:13.502620Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d619e2ab-6886-44f8-9a45-7f500f198bcc · inbound

Efficient Data Selection at Scale via Influence Distillation cites this paper.

Efficient Data Selection at Scale via Influence Distillation When Less is More: Investigating Data Pruning for Pretraining LLMs at Scale

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-07T14:25:18.051571Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ea4f463e-8f5a-43cf-a59f-4cf597277efc · inbound

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

ESLM: Risk-Averse Selective Language Modeling for Efficient Pretraining When Less is More: Investigating Data Pruning for Pretraining LLMs at Scale

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-07T14:09:48.284205Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b36ccb73-9dcf-40a7-b18e-44f33a2c34eb · inbound

GORACS: Group-level Optimal Transport-guided Coreset Selection for LLM-based Recommender Systems cites this paper.

GORACS: Group-level Optimal Transport-guided Coreset Selection for LLM-based Recommender Systems When Less is More: Investigating Data Pruning for Pretraining LLMs at Scale

Reference 45

Resolution: unresolved; no resolver link, observed 2026-08-07T10:56:21.826147Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 64bb2aec-abbc-4d16-aafe-645de5f60d18 · inbound

Training Dynamics Underlying Language Model Scaling Laws: Loss Deceleration and Zero-Sum Learning cites this paper.

Training Dynamics Underlying Language Model Scaling Laws: Loss Deceleration and Zero-Sum Learning When Less is More: Investigating Data Pruning for Pretraining LLMs at Scale

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-07T10:31:20.455874Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 59f6abf4-afe3-47c3-a6ba-d8b95481c1ed · inbound

Efficient dataset generation for machine learning perovskite alloys cites this paper.

Efficient dataset generation for machine learning perovskite alloys When Less is More: Investigating Data Pruning for Pretraining LLMs at Scale

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-07T10:19:07.465093Z

Source-reported events for the cited work

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Observation ca62f42a-f897-4865-96b0-edae64c870fa · inbound

Disentangling the Roles of Representation and Selection in Data Pruning cites this paper.

Disentangling the Roles of Representation and Selection in Data Pruning When Less is More: Investigating Data Pruning for Pretraining LLMs at Scale

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-06T20:15:24.011287Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6d07eb10-06d0-43e4-8429-1cb456aa0180 · inbound

Language Models Improve When Pretraining Data Matches Target Tasks cites this paper.

Language Models Improve When Pretraining Data Matches Target Tasks When Less is More: Investigating Data Pruning for Pretraining LLMs at Scale

Reference 63

Resolution: unresolved; no resolver link, observed 2026-08-06T16:53:11.656872Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f1ed7484-fbb0-434d-9557-1e0cf813a131 · inbound

ADMIRE-BayesOpt: Accelerated Data MIxture RE-weighting for Language Models with Bayesian Optimization cites this paper.

ADMIRE-BayesOpt: Accelerated Data MIxture RE-weighting for Language Models with Bayesian Optimization When Less is More: Investigating Data Pruning for Pretraining LLMs at Scale

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-05T20:00:05.194995Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2e348e6e-7e5a-4f43-9d9d-a883ad6a9fef · inbound

GIST: Targeted Data Selection for Instruction Tuning via Coupled Optimization Geometry cites this paper.

GIST: Targeted Data Selection for Instruction Tuning via Coupled Optimization Geometry When Less is More: Investigating Data Pruning for Pretraining LLMs at Scale

Reference 3

Resolution: verified exact; arxiv_id, observed 2026-05-21T12:30:07.825970Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 02f8e2ee-dc95-42cb-a6ab-97b1549804ad · inbound

OPERA: Online Data Pruning for Efficient Retrieval Model Adaptation cites this paper.

OPERA: Online Data Pruning for Efficient Retrieval Model Adaptation When Less is More: Investigating Data Pruning for Pretraining LLMs at Scale

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-03T02:34:19.737306Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f79798d1-047b-49c3-a62f-082c3e17873d · inbound

UI-Oceanus: Scaling GUI Agents with Synthetic Environmental Dynamics cites this paper.

UI-Oceanus: Scaling GUI Agents with Synthetic Environmental Dynamics When Less is More: Investigating Data Pruning for Pretraining LLMs at Scale

Reference 28

Resolution: verified exact; arxiv_id, observed 2026-05-16T03:00:31.597557Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation bec684eb-a500-4351-a15f-776c0af3754b · inbound

A Systematic Framework for Tabular Data Disentanglement cites this paper.

A Systematic Framework for Tabular Data Disentanglement When Less is More: Investigating Data Pruning for Pretraining LLMs at Scale

Reference 20

Resolution: verified exact; arxiv_id, observed 2026-05-11T00:30:55.343421Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation a3ddf1a1-2cb4-416e-b88d-752280d835eb · inbound

GRACE: A Dynamic Coreset Selection Framework for Large Language Model Optimization cites this paper.

GRACE: A Dynamic Coreset Selection Framework for Large Language Model Optimization When Less is More: Investigating Data Pruning for Pretraining LLMs at Scale

Reference 57

Resolution: verified exact; arxiv_id, observed 2026-05-10T20:30:49.021001Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 3a677dd1-e712-45c9-805e-2f19681436fb · inbound

Representation-Guided Parameter-Efficient LLM Unlearning cites this paper.

Representation-Guided Parameter-Efficient LLM Unlearning When Less is More: Investigating Data Pruning for Pretraining LLMs at Scale

Reference 135

Resolution: verified exact; arxiv_id, observed 2026-05-10T06:06:19.197284Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 42d6e4f0-05f0-4d7e-b871-71d586e13301 · inbound

InvEvolve: Evolving White-Box Inventory Policies via Large Language Models with Performance Guarantees cites this paper.

InvEvolve: Evolving White-Box Inventory Policies via Large Language Models with Performance Guarantees When Less is More: Investigating Data Pruning for Pretraining LLMs at Scale

Reference 159

Resolution: verified exact; arxiv_id, observed 2026-05-11T15:31:07.878353Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 06f0d75f-3540-4396-bc2f-322592cb7c9d · inbound

InvEvolve: Evolving White-Box Inventory Policies via Large Language Models with Performance Guarantees cites this paper.

InvEvolve: Evolving White-Box Inventory Policies via Large Language Models with Performance Guarantees When Less is More: Investigating Data Pruning for Pretraining LLMs at Scale

Reference 159

Resolution: verified exact; arxiv_id, observed 2026-05-12T02:41:17.710831Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 9b194a4b-747d-408c-854b-b7680421d8ca · inbound

Rethinking Data Curation in LLM Training: Online Reweighting Offers Better Generalization than Offline Methods cites this paper.

Rethinking Data Curation in LLM Training: Online Reweighting Offers Better Generalization than Offline Methods When Less is More: Investigating Data Pruning for Pretraining LLMs at Scale

Reference 30

Resolution: metadata mismatch; arxiv_id, observed 2026-05-10T07:11:53.392676Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation b00f21c8-e6b6-4814-ac7b-59860a533759 · inbound

Data Difficulty and the Generalization--Extrapolation Tradeoff in LLM Fine-Tuning cites this paper.

Data Difficulty and the Generalization--Extrapolation Tradeoff in LLM Fine-Tuning When Less is More: Investigating Data Pruning for Pretraining LLMs at Scale

Reference 12

Resolution: verified exact; arxiv_id, observed 2026-05-14T20:07:54.465343Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation d5555e70-5a61-4bd6-98b2-0169e925a5cf · inbound

What Really Improves Mathematical Reasoning: Structured Reasoning Signals Beyond Pure Code cites this paper.

What Really Improves Mathematical Reasoning: Structured Reasoning Signals Beyond Pure Code When Less is More: Investigating Data Pruning for Pretraining LLMs at Scale

Reference 26

Resolution: verified exact; arxiv_id, observed 2026-05-20T05:08:05.057764Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation db30ffd2-cf6e-4225-839f-d856a63944c6 · inbound

Unified Data Selection for LLM Reasoning cites this paper.

Unified Data Selection for LLM Reasoning When Less is More: Investigating Data Pruning for Pretraining LLMs at Scale

Reference 45

Resolution: verified exact; arxiv_id, observed 2026-05-22T05:34:40.137379Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation ab1d612f-3fa9-44d0-923c-f018258698cf · inbound

Single-Rollout Hidden-State Dynamics for Training-Free RLVR Data Selection cites this paper.

Single-Rollout Hidden-State Dynamics for Training-Free RLVR Data Selection When Less is More: Investigating Data Pruning for Pretraining LLMs at Scale

Reference 14

Resolution: verified exact; arxiv_id, observed 2026-06-29T14:13:30.110956Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 9977346f-1710-4a74-a843-032e2e0f72e6 · inbound

OrderDP: A Theoretically Guaranteed Lossless Dynamic Data Pruning Framework cites this paper.

OrderDP: A Theoretically Guaranteed Lossless Dynamic Data Pruning Framework When Less is More: Investigating Data Pruning for Pretraining LLMs at Scale

Reference 111

Resolution: verified exact; arxiv_id, observed 2026-07-02T23:07:27.218492Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 8b75c3a5-0934-4ce1-a63d-ccd01945265c · inbound

Data Selection Through Iterative Self-Filtering for Vision-Language Settings cites this paper.

Data Selection Through Iterative Self-Filtering for Vision-Language Settings When Less is More: Investigating Data Pruning for Pretraining LLMs at Scale

Reference 195

Resolution: verified exact; arxiv_id, observed 2026-07-04T09:49:44.946514Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation ddfb4caa-9c82-4503-86fc-8301fe8837a6 · inbound

Internal Data Repetition Destroys Language Models cites this paper.

Internal Data Repetition Destroys Language Models When Less is More: Investigating Data Pruning for Pretraining LLMs at Scale

Reference 60

Resolution: verified exact; arxiv_id, observed 2026-07-04T16:49:57.884375Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 870d7ae7-8728-4ea3-8419-a661c6fd1a08 · inbound

On-Policy Self-Distillation with Sampled Demonstrations Reduces Output Diversity cites this paper.

On-Policy Self-Distillation with Sampled Demonstrations Reduces Output Diversity When Less is More: Investigating Data Pruning for Pretraining LLMs at Scale

Reference 216

Resolution: verified exact; arxiv_id, observed 2026-07-04T20:50:12.707543Z

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No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 94a9d6c8-a587-4be7-8fed-9f93f61025bc · inbound

From Data to Device: ELMOD An Efficient German-First 2.7B Language Model for Mobile Inference cites this paper.

From Data to Device: ELMOD An Efficient German-First 2.7B Language Model for Mobile Inference When Less is More: Investigating Data Pruning for Pretraining LLMs at Scale

Reference 23

Resolution: unresolved; no resolver link, observed 2026-07-31T11:19:34.319148Z

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Unavailable: canonical work link unavailable.