Paper Citation Record · LEDGER

Sparse Gradient Compression for Fine-Tuning Large Language Models

As of 10 August 2026, this Paper Citation Record lists 43 of 43 outbound references and 3 inbound Pith citation observations for arXiv:2502.00311.

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

pith.paper-citation-record.v1
2502.00311 v1

Coverage vector

measured 43 of 43 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-09T19:36:17.881647Z

measured 46 of 46 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 3 of 3 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-21T08:39:31.911497Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-21T08:39:53.214305Z

Reference resolution

43 of 43 outbound references displayed

verified exact1
verified fuzzy5
unresolved37
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 86eab819-69de-4f1d-917a-13aed460c351 · outbound

This paper cites Qwen Technical Report.

Sparse Gradient Compression for Fine-Tuning Large Language Models Qwen Technical Report

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-09T19:36:17.737864Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f9a38e62-7f78-450e-8323-4f08daaf98fc · outbound

This paper cites Decoding by Linear Programming.

Sparse Gradient Compression for Fine-Tuning Large Language Models Decoding by Linear Programming

Reference 2

Resolution: verified exact; local_arxiv, observed 2026-08-09T19:36:18.594504Z

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 f0270873-4c13-4bb8-a157-054fede2c862 · outbound

This paper cites Robust Uncertainty Principles: Exact Signal Reconstruction from Highly Incomplete Frequency Information.

Sparse Gradient Compression for Fine-Tuning Large Language Models Robust Uncertainty Principles: Exact Signal Reconstruction from Highly Incomplete Frequency Information

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-09T19:36:17.746061Z

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Observation 3176bd5c-d616-43ea-b46f-f4d4311541b7 · outbound

This paper cites The restricted isometry property and its implications for compressed sensing.

Sparse Gradient Compression for Fine-Tuning Large Language Models The restricted isometry property and its implications for compressed sensing

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T19:36:18.673079Z

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 e873054e-003c-4e52-a961-6a883516badd · outbound

This paper cites PaLM: Scaling Language Modeling with Pathways.

Sparse Gradient Compression for Fine-Tuning Large Language Models PaLM: Scaling Language Modeling with Pathways

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-09T19:36:17.753623Z

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Observation 565bf59a-aa08-4b5c-b984-8090543af5ee · outbound

This paper cites BoolQ: Exploring the Surprising Difficulty of Natural Yes/No Questions.

Sparse Gradient Compression for Fine-Tuning Large Language Models BoolQ: Exploring the Surprising Difficulty of Natural Yes/No Questions

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-09T19:36:17.757293Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d9155d89-d4a7-4873-aeba-a42b3ed7f3d1 · outbound

This paper cites QLoRA: Efficient Finetuning of Quantized LLMs.

Sparse Gradient Compression for Fine-Tuning Large Language Models QLoRA: Efficient Finetuning of Quantized LLMs

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-09T19:36:17.761323Z

Source-reported events for the cited work

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Observation 97fcdb8c-9ab9-4957-821c-aba9cb433f1e · outbound

This paper cites Parameter-efficient fine-tuning of large-scale pre-trained language models.

Sparse Gradient Compression for Fine-Tuning Large Language Models Parameter-efficient fine-tuning of large-scale pre-trained language models

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-09T19:36:17.765020Z

Source-reported events for the cited work

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Observation 5befa8c3-693d-47d9-9880-7c5f63b5281c · outbound

This paper cites Compressed sensing.

Sparse Gradient Compression for Fine-Tuning Large Language Models Compressed sensing

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T19:36:18.657363Z

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 3dfbfe69-c703-489b-ad54-0bc353df4bd9 · outbound

This paper cites The Llama 3 Herd of Models.

Sparse Gradient Compression for Fine-Tuning Large Language Models The Llama 3 Herd of Models

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-09T19:36:17.771445Z

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Observation 9ad3f6fb-a352-4868-96ce-165fa8fa291f · outbound

This paper cites Parameter-Efficient Fine-Tuning for Large Models: A Comprehensive Survey.

Sparse Gradient Compression for Fine-Tuning Large Language Models Parameter-Efficient Fine-Tuning for Large Models: A Comprehensive Survey

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-09T19:36:17.774791Z

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Observation 41686e11-7732-4b46-90fc-326d16a0dfc8 · outbound

This paper cites Flora: Low-Rank Adapters Are Secretly Gradient Compressors.

Sparse Gradient Compression for Fine-Tuning Large Language Models Flora: Low-Rank Adapters Are Secretly Gradient Compressors

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-09T19:36:17.778015Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5ffcbcec-0c0b-4b4a-9037-0517cf6b2785 · outbound

This paper cites LoRA+: Efficient Low Rank Adaptation of Large Models.

Sparse Gradient Compression for Fine-Tuning Large Language Models LoRA+: Efficient Low Rank Adaptation of Large Models

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-09T19:36:17.781296Z

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Observation 82664719-81a5-4b27-b7f9-20b7b9769820 · outbound

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

Sparse Gradient Compression for Fine-Tuning Large Language Models Towards a Unified View of Parameter-Efficient Transfer Learning

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-09T19:36:17.784720Z

Source-reported events for the cited work

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Observation b9199260-0724-493f-910c-7440023d6314 · outbound

This paper cites Parameter-Efficient Transfer Learning for NLP.

Sparse Gradient Compression for Fine-Tuning Large Language Models Parameter-Efficient Transfer Learning for NLP

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-09T19:36:17.787922Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 086975dd-83d1-4b71-9038-8d3cc64a7ad2 · outbound

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

Sparse Gradient Compression for Fine-Tuning Large Language Models LoRA: Low-Rank Adaptation of Large Language Models

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-09T19:36:17.791270Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 15ddbe4e-cd0c-4b0e-a589-a0115203258f · outbound

This paper cites LLM-Adapters: An Adapter Family for Parameter-Efficient Fine-Tuning of Large Language Models.

Sparse Gradient Compression for Fine-Tuning Large Language Models LLM-Adapters: An Adapter Family for Parameter-Efficient Fine-Tuning of Large Language Models

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-09T19:36:17.794346Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f869e641-dcec-47d6-9b42-c81201e5adce · outbound

This paper cites LoRA Training in the NTK Regime has No Spurious Local Minima.

Sparse Gradient Compression for Fine-Tuning Large Language Models LoRA Training in the NTK Regime has No Spurious Local Minima

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-09T19:36:17.797970Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation abb5675f-ddcb-4b29-b9bc-07f2faa32034 · outbound

This paper cites Adam: A Method for Stochastic Optimization.

Sparse Gradient Compression for Fine-Tuning Large Language Models Adam: A Method for Stochastic Optimization

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-09T19:36:17.801369Z

Source-reported events for the cited work

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Observation e8f65eb4-cc9c-485e-9a4d-bbc8718e6ad4 · outbound

This paper cites VeRA: Vector-based Random Matrix Adaptation.

Sparse Gradient Compression for Fine-Tuning Large Language Models VeRA: Vector-based Random Matrix Adaptation

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-09T19:36:17.804534Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 07e24b47-0e72-4b28-b870-144ed38954e7 · outbound

This paper cites The Power of Scale for Parameter-Efficient Prompt Tuning.

Sparse Gradient Compression for Fine-Tuning Large Language Models The Power of Scale for Parameter-Efficient Prompt Tuning

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-09T19:36:17.808167Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ff7ced89-bc9c-4e65-ad51-d3eb87bf2a72 · outbound

This paper cites Prefix-Tuning: Optimizing Continuous Prompts for Generation.

Sparse Gradient Compression for Fine-Tuning Large Language Models Prefix-Tuning: Optimizing Continuous Prompts for Generation

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-09T19:36:17.811398Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6d1fc8ae-5ae8-4910-b94c-4723451ebeef · outbound

This paper cites Memory-Efficient LLM Training with Online Subspace Descent.

Sparse Gradient Compression for Fine-Tuning Large Language Models Memory-Efficient LLM Training with Online Subspace Descent

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-09T19:36:17.814744Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f4e409d6-bc4b-4f3c-bb62-fbbbbf29ffda · outbound

This paper cites DoRA: Weight-Decomposed Low-Rank Adaptation.

Sparse Gradient Compression for Fine-Tuning Large Language Models DoRA: Weight-Decomposed Low-Rank Adaptation

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-09T19:36:17.818221Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b90e963b-3618-44b8-9275-5b7cd3163ca9 · outbound

This paper cites P-Tuning v2: Prompt Tuning Can Be Comparable to Fine-tuning Universally Across Scales and Tasks.

Sparse Gradient Compression for Fine-Tuning Large Language Models P-Tuning v2: Prompt Tuning Can Be Comparable to Fine-tuning Universally Across Scales and Tasks

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-09T19:36:17.821607Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4a77fbeb-75c0-4720-8086-f3bf17189252 · outbound

This paper cites Decoupled Weight Decay Regularization.

Sparse Gradient Compression for Fine-Tuning Large Language Models Decoupled Weight Decay Regularization

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-09T19:36:17.825287Z

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Unavailable: canonical work link unavailable.

Observation 53f3b197-a9d7-4fb6-88a8-6893ff423014 · outbound

This paper cites Parameter-efficient Multi-task Fine-tuning for Transformers via Shared Hypernetworks.

Sparse Gradient Compression for Fine-Tuning Large Language Models Parameter-efficient Multi-task Fine-tuning for Transformers via Shared Hypernetworks

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-09T19:36:17.828868Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d9eb9b1d-d850-487d-9d07-333704c92bba · outbound

This paper cites A Survey on LoRA of Large Language Models.

Sparse Gradient Compression for Fine-Tuning Large Language Models A Survey on LoRA of Large Language Models

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-09T19:36:17.832226Z

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Observation 32cc5ac5-9d2a-4f24-bfcd-23f2189d5bb8 · outbound

This paper cites A review of sparse recovery algorithms.

Sparse Gradient Compression for Fine-Tuning Large Language Models A review of sparse recovery algorithms

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T19:36:18.647701Z

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 073f25c4-e3ab-406a-b07c-2c51facf3800 · outbound

This paper cites Orthogonal matching pursuit: Recursive function approximation with applications to wavelet decomposition.

Sparse Gradient Compression for Fine-Tuning Large Language Models Orthogonal matching pursuit: Recursive function approximation with applications to wavelet decomposition

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T19:36:18.636894Z

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 c9da6903-5413-43e9-a894-a760d3e58285 · outbound

This paper cites AdapterFusion: Non-Destructive Task Composition for Transfer Learning.

Sparse Gradient Compression for Fine-Tuning Large Language Models AdapterFusion: Non-Destructive Task Composition for Transfer Learning

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-09T19:36:17.841608Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 10cbf498-dbd3-44eb-a1e0-ce62ed2dff9d · outbound

This paper cites Accurate LoRA-Finetuning Quantization of LLMs via Information Retention.

Sparse Gradient Compression for Fine-Tuning Large Language Models Accurate LoRA-Finetuning Quantization of LLMs via Information Retention

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-09T19:36:17.845075Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e1906722-64d4-49ad-824f-fa9244388ecb · outbound

This paper cites Sparse is Enough in Fine-tuning Pre-trained Large Language Models.

Sparse Gradient Compression for Fine-Tuning Large Language Models Sparse is Enough in Fine-tuning Pre-trained Large Language Models

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-09T19:36:17.848341Z

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Observation bddf558a-1ba0-4a2c-9c13-7bf580d03ef6 · outbound

This paper cites Sparsified SGD with Memory.

Sparse Gradient Compression for Fine-Tuning Large Language Models Sparsified SGD with Memory

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-09T19:36:17.852557Z

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Observation adcc9dc9-f84b-44b9-b18e-ccd3059dd9d1 · outbound

This paper cites Hashimoto.

Sparse Gradient Compression for Fine-Tuning Large Language Models Hashimoto

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-09T19:36:17.855812Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3a5f929b-0fbc-4de4-a96f-c84150bae191 · outbound

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

Sparse Gradient Compression for Fine-Tuning Large Language Models Llama 2: Open Foundation and Fine-Tuned Chat Models

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-09T19:36:17.858941Z

Source-reported events for the cited work

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Observation e386ad5b-b2c5-4547-bc37-eb8e65d52e42 · outbound

This paper cites Cg-fedllm: How to compress gradients in federated fune-tuning for large language models, 2024.

Sparse Gradient Compression for Fine-Tuning Large Language Models Cg-fedllm: How to compress gradients in federated fune-tuning for large language models, 2024

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-09T19:36:17.862154Z

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Unavailable: canonical work link unavailable.

Observation 45e386fb-90c4-46cf-a2be-b0bb72a60b23 · outbound

This paper cites Chain of LoRA: Efficient Fine-tuning of Language Models via Residual Learning.

Sparse Gradient Compression for Fine-Tuning Large Language Models Chain of LoRA: Efficient Fine-tuning of Language Models via Residual Learning

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-09T19:36:17.865189Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6d8d5107-4a19-4ef2-905e-5c8785b0054a · outbound

This paper cites Bitfit: Simple parameter-efficient fine-tuning for transformer-based masked language-models, 2022.

Sparse Gradient Compression for Fine-Tuning Large Language Models Bitfit: Simple parameter-efficient fine-tuning for transformer-based masked language-models, 2022

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-09T19:36:17.868567Z

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Observation eedd31d1-607d-45ea-9fb8-130322623808 · outbound

This paper cites AdaLoRA: Adaptive Budget Allocation for Parameter-Efficient Fine-Tuning.

Sparse Gradient Compression for Fine-Tuning Large Language Models AdaLoRA: Adaptive Budget Allocation for Parameter-Efficient Fine-Tuning

Reference 40

Resolution: unresolved; no resolver link, observed 2026-08-09T19:36:17.871740Z

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Unavailable: canonical work link unavailable.

Observation 2292b965-8f36-4e1d-aaa0-77ac41752a46 · outbound

This paper cites GaLore: Memory-Efficient LLM Training by Gradient Low-Rank Projection.

Sparse Gradient Compression for Fine-Tuning Large Language Models GaLore: Memory-Efficient LLM Training by Gradient Low-Rank Projection

Reference 41

Resolution: unresolved; no resolver link, observed 2026-08-09T19:36:17.874979Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f0d41f64-a43a-4c4f-ad2a-9be61e9bd0a7 · outbound

This paper cites Efficient implementations for orthogonal matching pursuit.

Sparse Gradient Compression for Fine-Tuning Large Language Models Efficient implementations for orthogonal matching pursuit

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T19:36:18.621056Z

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 a385b9dc-d055-4eae-a8d6-18067364d007 · outbound

This paper cites write newline.

Sparse Gradient Compression for Fine-Tuning Large Language Models write newline

Reference 43

Resolution: unresolved; no resolver link, observed 2026-08-09T19:36:17.881647Z

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Unavailable: canonical work link unavailable.

Pith citing papers

Observation a3331bde-6d91-4d52-ba99-2af783afbbaa · inbound

MLorc: Momentum Low-rank Compression for Memory Efficient Large Language Model Adaptation cites this paper.

MLorc: Momentum Low-rank Compression for Memory Efficient Large Language Model Adaptation Sparse Gradient Compression for Fine-Tuning Large Language Models

Reference 15

Resolution: metadata mismatch; arxiv_id, observed 2026-05-19T10:52:15.108322Z

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 27061e32-7b68-4a2c-90b1-f173f2dba996 · inbound

ROSE: Rollout On Serving GPUs via Cooperative Elasticity for Agentic RL cites this paper.

ROSE: Rollout On Serving GPUs via Cooperative Elasticity for Agentic RL Sparse Gradient Compression for Fine-Tuning Large Language Models

Reference 84

Resolution: verified exact; arxiv_id, observed 2026-05-11T21:31:16.046949Z

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 189fef69-51a0-4caf-9c2d-a20fc04556ed · inbound

ROSE: Rollout On Serving GPUs via Cooperative Elasticity for Agentic RL cites this paper.

ROSE: Rollout On Serving GPUs via Cooperative Elasticity for Agentic RL Sparse Gradient Compression for Fine-Tuning Large Language Models

Reference 83

Resolution: verified exact; arxiv_id, observed 2026-05-21T08:39:53.216763Z

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.