Paper Citation Record · LEDGER

How to Train Data-Efficient LLMs

As of 15 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 36 inbound Pith citation observations for arXiv:2402.09668.

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

pith.paper-citation-record.v1
2402.09668 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 36 of 36 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-14T06:32:32.682623+00:00

measured 36 of 36 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-12T19:40:15.672658Z

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
verified fuzzy0
unresolved0
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malformed identifier0
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External citation measurements

4
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 3d59eb21-f235-4807-978f-cb47c3275d11 · inbound

A Survey of Large Language Models cites this paper.

A Survey of Large Language Models How to Train Data-Efficient LLMs

Reference 237

Resolution: metadata mismatch; arxiv_id, observed 2026-05-10T22:46:40.315894Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 792957dc-e756-4ca0-bad0-21339c6cd949 · inbound

InternLM2 Technical Report cites this paper.

InternLM2 Technical Report How to Train Data-Efficient LLMs

Reference 74

Resolution: metadata mismatch; arxiv_id, observed 2026-05-15T11:44:38.337372Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 149a544f-2fdb-4df2-bf3a-ca4639ca9b4b · inbound

DataComp-LM: In search of the next generation of training sets for language models cites this paper.

DataComp-LM: In search of the next generation of training sets for language models How to Train Data-Efficient LLMs

Reference 157

Resolution: metadata mismatch; arxiv_id, observed 2026-05-17T22:58:17.197578Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 61de4bd6-3171-4400-8060-c1e13931fd20 · inbound

Efficient Alignment of Large Language Models via Data Sampling cites this paper.

Efficient Alignment of Large Language Models via Data Sampling How to Train Data-Efficient LLMs

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-12T19:40:15.672658Z

Source-reported events for the cited work

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Observation c3d4efd7-58b9-4bca-be02-88d5c24569b8 · inbound

LP Data Pipeline: Lightweight, Purpose-driven Data Pipeline for Large Language Models cites this paper.

LP Data Pipeline: Lightweight, Purpose-driven Data Pipeline for Large Language Models How to Train Data-Efficient LLMs

Reference 41

Resolution: unresolved; no resolver link, observed 2026-08-12T18:45:47.427739Z

Source-reported events for the cited work

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Observation 34c8fa16-7965-4daa-aa31-b2f4b70e9ac3 · inbound

Training Bilingual LMs with Data Constraints in the Targeted Language cites this paper.

Training Bilingual LMs with Data Constraints in the Targeted Language How to Train Data-Efficient LLMs

Reference 50

Resolution: unresolved; no resolver link, observed 2026-08-12T17:04:34.463318Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ec3b47f7-da73-41fe-a6ce-030286bc9153 · inbound

7B Fully Open Source Moxin-LLM/VLM -- From Pretraining to GRPO-based Reinforcement Learning Enhancement cites this paper.

7B Fully Open Source Moxin-LLM/VLM -- From Pretraining to GRPO-based Reinforcement Learning Enhancement How to Train Data-Efficient LLMs

Reference 59

Resolution: unresolved; no resolver link, observed 2026-08-11T20:27:18.187919Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d1c50baa-eef8-43cc-a5b4-0be00d64494a · inbound

Preference Curriculum: LLMs Should Always Be Pretrained on Their Preferred Data cites this paper.

Preference Curriculum: LLMs Should Always Be Pretrained on Their Preferred Data How to Train Data-Efficient LLMs

Reference 2024

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 102ae0a7-155c-48e5-80d8-c6c9c7ab32da · inbound

FIRE: Flexible Integration of Data Quality Ratings for Effective Pre-Training cites this paper.

FIRE: Flexible Integration of Data Quality Ratings for Effective Pre-Training How to Train Data-Efficient LLMs

Reference 2024

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fd3cbe97-8165-41a3-9aa7-4928f63c3f7a · inbound

FRAME: Boosting LLMs with A Four-Quadrant Multi-Stage Pretraining Strategy cites this paper.

FRAME: Boosting LLMs with A Four-Quadrant Multi-Stage Pretraining Strategy How to Train Data-Efficient LLMs

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-08T18:58:34.553634Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1d43c75f-11a3-48e7-8928-a35bf4795c4b · inbound

PiKE: Adaptive Data Mixing for Large-Scale Multi-Task Learning Under Low Gradient Conflicts cites this paper.

PiKE: Adaptive Data Mixing for Large-Scale Multi-Task Learning Under Low Gradient Conflicts How to Train Data-Efficient LLMs

Reference 53

Resolution: unresolved; no resolver link, observed 2026-08-08T16:20:38.347017Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2e7b6d1c-21d2-49e5-8281-2fa89251ff4d · inbound

Gemma 3 Technical Report cites this paper.

Gemma 3 Technical Report How to Train Data-Efficient LLMs

Reference 36

Resolution: verified exact; arxiv_id, observed 2026-05-22T22:22:12.189477Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 008d596f-f874-4fec-ae02-f2b8d97f73f7 · inbound

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

Enhancing LLMs via High-Knowledge Data Selection How to Train Data-Efficient LLMs

Reference 41

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 75691a37-0af0-4eb6-8cbb-6578d75e4297 · inbound

FisherSFT: Data-Efficient Supervised Fine-Tuning of Language Models Using Information Gain cites this paper.

FisherSFT: Data-Efficient Supervised Fine-Tuning of Language Models Using Information Gain How to Train Data-Efficient LLMs

Reference 41

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7be11a93-fc4f-456a-b1ce-c09431db1d5d · inbound

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

ESLM: Risk-Averse Selective Language Modeling for Efficient Pretraining How to Train Data-Efficient LLMs

Reference 46

Resolution: unresolved; no resolver link, observed 2026-08-07T14:09:49.740308Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8e5f661e-1df7-4c21-b0f1-6e0f8d337875 · inbound

Agents Require Metacognitive and Strategic Reasoning to Succeed in the Coming Labor Markets cites this paper.

Agents Require Metacognitive and Strategic Reasoning to Succeed in the Coming Labor Markets How to Train Data-Efficient LLMs

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-07T14:02:21.726501Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 45a2bb00-27e7-413c-aad1-04bfb0365e04 · inbound

Judging Quality Across Languages: A Multilingual Approach to Pretraining Data Filtering with Language Models cites this paper.

Judging Quality Across Languages: A Multilingual Approach to Pretraining Data Filtering with Language Models How to Train Data-Efficient LLMs

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-07T13:19:31.769180Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation baf9518d-8283-4d8e-9693-627a73a6bf4c · inbound

Truly Self-Improving Agents Require Intrinsic Metacognitive Learning cites this paper.

Truly Self-Improving Agents Require Intrinsic Metacognitive Learning How to Train Data-Efficient LLMs

Reference 61

Resolution: unresolved; no resolver link, observed 2026-08-07T10:28:22.198008Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9d290ed7-dc77-45a3-b4e6-29d20b1418fa · inbound

Ming-Omni: A Unified Multimodal Model for Perception and Generation cites this paper.

Ming-Omni: A Unified Multimodal Model for Perception and Generation How to Train Data-Efficient LLMs

Reference 2012

Resolution: unresolved; no resolver link, observed 2026-08-07T04:58:09.750924Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2a1f4957-80ec-4cd3-a2fd-c1a72c8777ca · inbound

Assessing the Role of Data Quality in Training Bilingual Language Models cites this paper.

Assessing the Role of Data Quality in Training Bilingual Language Models How to Train Data-Efficient LLMs

Reference 46

Resolution: unresolved; no resolver link, observed 2026-08-07T00:42:57.821522Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 98bac85f-4f08-4101-acf8-8a2b1e0ef229 · inbound

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

Disentangling the Roles of Representation and Selection in Data Pruning How to Train Data-Efficient LLMs

Reference 35

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Source-reported events for the cited work

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Observation f1040ee3-a77b-4e43-8c1c-ea8522bbea7f · inbound

Efficient Training of Deep Networks using Guided Spectral Data Selection: A Step Toward Learning What You Need cites this paper.

Efficient Training of Deep Networks using Guided Spectral Data Selection: A Step Toward Learning What You Need How to Train Data-Efficient LLMs

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-06T19:55:46.325370Z

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Unavailable: canonical work link unavailable.

Observation 6d867eea-4249-4856-8592-b3db67339c98 · inbound

Sub-Scaling Laws: On the Role of Data Density and Training Strategies in LLMs cites this paper.

Sub-Scaling Laws: On the Role of Data Density and Training Strategies in LLMs How to Train Data-Efficient LLMs

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-06T17:56:44.081602Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d6c6eb5c-08c4-4a4b-a6c8-30527e315b02 · inbound

Beyond Traditional Algorithms: Leveraging LLMs for Accurate Cross-Border Entity Identification cites this paper.

Beyond Traditional Algorithms: Leveraging LLMs for Accurate Cross-Border Entity Identification How to Train Data-Efficient LLMs

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-06T17:21:32.871288Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8490cd5d-1c4f-4bab-95f1-f8eca72731b9 · inbound

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

Language Models Improve When Pretraining Data Matches Target Tasks How to Train Data-Efficient LLMs

Reference 86

Resolution: unresolved; no resolver link, observed 2026-08-06T16:53:13.006591Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3a86b5a3-61a3-4750-a132-2cb73815b823 · inbound

LAMDAS: LLM as an Implicit Classifier for Domain-specific Data Selection cites this paper.

LAMDAS: LLM as an Implicit Classifier for Domain-specific Data Selection How to Train Data-Efficient LLMs

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-04T23:33:42.206386Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7860f5b1-4c1e-4780-86e1-89b7fa135eb3 · inbound

An Empirical Study on Influence-Based Pretraining Data Selection for Code Large Language Models cites this paper.

An Empirical Study on Influence-Based Pretraining Data Selection for Code Large Language Models How to Train Data-Efficient LLMs

Reference 35

Resolution: verified exact; arxiv_id, observed 2026-05-11T05:16:04.764330Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 6b912b53-402d-4d00-bb74-0e15c36c04ac · inbound

Cram Less to Fit More: Training Data Pruning Improves Memorization of Facts cites this paper.

Cram Less to Fit More: Training Data Pruning Improves Memorization of Facts How to Train Data-Efficient LLMs

Reference 74

Resolution: metadata mismatch; arxiv_id, observed 2026-05-11T06:15:59.080239Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 2b4a269f-1cbb-4eaf-b0d9-3a16f4405fe2 · inbound

KoCo: Conditioning Language Model Pre-training on Knowledge Coordinates cites this paper.

KoCo: Conditioning Language Model Pre-training on Knowledge Coordinates How to Train Data-Efficient LLMs

Reference 2

Resolution: metadata mismatch; arxiv_id, observed 2026-05-11T10:41:04.697353Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 6601df6f-9c38-41f9-ab46-d3096b43ba4e · inbound

DOSE: Data Selection for Multi-Modal LLMs via Off-the-Shelf Models cites this paper.

DOSE: Data Selection for Multi-Modal LLMs via Off-the-Shelf Models How to Train Data-Efficient LLMs

Reference 6

Resolution: metadata mismatch; arxiv_id, observed 2026-05-10T06:21:26.951063Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 98535e94-9a8d-4b8a-8662-34780db3a8c3 · inbound

Safactory: A Scalable Agentic Infrastructure for Training Trustworthy Autonomous Intelligence cites this paper.

Safactory: A Scalable Agentic Infrastructure for Training Trustworthy Autonomous Intelligence How to Train Data-Efficient LLMs

Reference 74

Resolution: metadata mismatch; arxiv_id, observed 2026-05-11T20:11:08.529589Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation af26c0cc-ad3e-43fe-9683-2353c9ae1b0d · inbound

Safactory: A Scalable Agentic Infrastructure for Training Trustworthy Autonomous Intelligence cites this paper.

Safactory: A Scalable Agentic Infrastructure for Training Trustworthy Autonomous Intelligence How to Train Data-Efficient LLMs

Reference 74

Resolution: metadata mismatch; arxiv_id, observed 2026-05-11T05:00:54.594603Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 9fe4cc44-6781-46ce-ab2f-60a76e197dfd · inbound

Accelerated Relax-and-Round for Concave Coverage Problems cites this paper.

Accelerated Relax-and-Round for Concave Coverage Problems How to Train Data-Efficient LLMs

Reference 39

Resolution: metadata mismatch; arxiv_id, observed 2026-05-11T05:05:57.094895Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation ad0f918b-ca1e-4b43-a0a9-df8466e9b472 · inbound

Reflections and New Directions for Human-Centered Large Language Models cites this paper.

Reflections and New Directions for Human-Centered Large Language Models How to Train Data-Efficient LLMs

Reference 27

Resolution: metadata mismatch; arxiv_id, observed 2026-05-11T04:25:56.843634Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 10576518-8575-43d8-9ea9-7263c128c47b · inbound

Efficient Test-Time Finetuning of LLMs via Convex Reconstruction and Gradient Caching cites this paper.

Efficient Test-Time Finetuning of LLMs via Convex Reconstruction and Gradient Caching How to Train Data-Efficient LLMs

Reference 42

Resolution: metadata mismatch; arxiv_id, observed 2026-06-29T09:03:15.950487Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 741108f7-c40c-483a-9b45-dd06febbe00e · inbound

Repetition Mismatch: Why Data Mixture Experiments Don't Scale and How to Fix Them cites this paper.

Repetition Mismatch: Why Data Mixture Experiments Don't Scale and How to Fix Them How to Train Data-Efficient LLMs

Reference 60

Resolution: metadata mismatch; arxiv_id, observed 2026-06-28T23:22:46.237668Z

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No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.