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Paper Citation Record · LEDGER

Variance-reduced Zeroth-Order Methods for Fine-Tuning Language Models

As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 10 inbound Pith citation observations for arXiv:2404.08080.

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

pith.paper-citation-record.v1
2404.08080 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 10 of 10 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 10 of 10 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-09T21:33:29.663460Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-03T17:08:43.678956Z

Reference resolution

0 of 0 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved0
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 9c7f7c78-a58c-4485-bbb4-846b836dc43f · inbound

TeZO: Empowering the Low-Rankness on the Temporal Dimension in the Zeroth-Order Optimization for Fine-tuning LLMs cites this paper.

TeZO: Empowering the Low-Rankness on the Temporal Dimension in the Zeroth-Order Optimization for Fine-tuning LLMs Variance-reduced Zeroth-Order Methods for Fine-Tuning Language Models

Reference 2022

Resolution
unresolved
no resolver link, observed 2026-08-09T21:33:29.663460Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-09T21:33:29.663460Z digest=sha256:352a392c5d82883e16b1bbbb70ec76cfe058b7f0638ab44051adc428a1af1a92

Observation 762db2e0-86aa-46b6-9993-bec36b85da3a · inbound

A Memory Efficient Randomized Subspace Optimization Method for Training Large Language Models cites this paper.

A Memory Efficient Randomized Subspace Optimization Method for Training Large Language Models Variance-reduced Zeroth-Order Methods for Fine-Tuning Language Models

Reference 2025

Resolution
unresolved
no resolver link, observed 2026-08-08T13:31:42.084774Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-08T13:31:42.084774Z digest=sha256:1efc6ea9750d2d9552ceaa90a9306c21ad08ddcb77e821ef88a97c7aa7c89070

Observation d585946b-e6b2-4c43-862d-de280c8bab51 · inbound

KerZOO: Kernel Function Informed Zeroth-Order Optimization for Accurate and Accelerated LLM Fine-Tuning cites this paper.

KerZOO: Kernel Function Informed Zeroth-Order Optimization for Accurate and Accelerated LLM Fine-Tuning Variance-reduced Zeroth-Order Methods for Fine-Tuning Language Models

Reference 16

Resolution
unresolved
no resolver link, observed 2026-08-07T14:29:33.120247Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T14:29:33.120247Z digest=sha256:8807a3609798bcc5dd186caa57e7d38f806d30f57876995df4e4bec494f31e6a

Observation f0eebf04-6407-49fc-a7c4-6ac2cd92c98b · inbound

CR-Net: Scaling Parameter-Efficient Training with Cross-Layer Low-Rank Structure cites this paper.

CR-Net: Scaling Parameter-Efficient Training with Cross-Layer Low-Rank Structure Variance-reduced Zeroth-Order Methods for Fine-Tuning Language Models

Reference 17

Resolution
verified exact
arxiv_id, observed 2026-05-18T14:52:41.152922Z

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.

source=pdf_text observed=2026-05-18T14:51:30.312509Z digest=sha256:3a8dc4c013cd40c6e1352388d54a266413c36fc8ab96314f68e6cd250aef642d

Observation 60596eb6-59f0-4530-866b-03a1d9d0c689 · inbound

Learning a Zeroth-Order Optimizer for Fine-Tuning LLMs cites this paper.

Learning a Zeroth-Order Optimizer for Fine-Tuning LLMs Variance-reduced Zeroth-Order Methods for Fine-Tuning Language Models

Reference 13

Resolution
unresolved
no resolver link, observed 2026-08-04T13:28:46.514611Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-04T13:28:46.514611Z digest=sha256:86e338500fd17cc71c04a85c8c72a3f401866f4252f7821e0884c56df406c541

Observation 33e937da-2f92-4758-95a1-85b43fec2d25 · inbound

Accelerating Zeroth-Order Spectral Optimization with Partial Orthogonalization from Power Iteration cites this paper.

Accelerating Zeroth-Order Spectral Optimization with Partial Orthogonalization from Power Iteration Variance-reduced Zeroth-Order Methods for Fine-Tuning Language Models

Reference 3

Resolution
verified exact
arxiv_id, observed 2026-05-12T07:41:42.876578Z

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.

source=pdf_text observed=2026-05-12T02:18:17.478817Z digest=sha256:5444a2bb3b64ccd8d65e9faa2ceba88c8f3c1475c6b7b11e5506c44c80213599

Observation 1314a47a-5f87-404e-9c82-969adf40c275 · inbound

Accelerating Zeroth-Order Spectral Optimization with Partial Orthogonalization from Power Iteration cites this paper.

Accelerating Zeroth-Order Spectral Optimization with Partial Orthogonalization from Power Iteration Variance-reduced Zeroth-Order Methods for Fine-Tuning Language Models

Reference 4

Resolution
verified exact
arxiv_id, observed 2026-05-19T17:33:09.420739Z

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.

source=pdf_text observed=2026-05-19T17:33:00.747846Z digest=sha256:9e947f55e79bf2d5213ca953e094cf7842bdedc93919385623875d3cc545ee47

Observation 33c6d116-37e8-4f3b-b9ac-1fa2fa7f43df · inbound

Position: Zeroth-Order Optimization in Deep Learning Is Underexplored, Not Underpowered cites this paper.

Position: Zeroth-Order Optimization in Deep Learning Is Underexplored, Not Underpowered Variance-reduced Zeroth-Order Methods for Fine-Tuning Language Models

Reference 51

Resolution
verified exact
arxiv_id, observed 2026-05-20T21:23:44.510194Z

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.

source=arxiv_source observed=2026-05-20T21:19:55.074853Z digest=sha256:3bd6598f0cd5894f6da9dac3b527140dfdf9f065c378c38767aa3ed5a1085921

Observation 8f7cafd0-0d0b-46f6-85a9-6dbb173bfc19 · inbound

Zero-order Parameter-free Optimization for LMO-based Methods: Novel Approach for Efficient Fine-tuning cites this paper.

Zero-order Parameter-free Optimization for LMO-based Methods: Novel Approach for Efficient Fine-tuning Variance-reduced Zeroth-Order Methods for Fine-Tuning Language Models

Reference 43

Resolution
verified exact
arxiv_id, observed 2026-07-03T17:08:43.680445Z

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.

source=pdf_text observed=2026-06-27T04:33:10.554853Z digest=sha256:c6e6e8d3ffa18d9c48c9dd9863cbea67d0414de1b3b22ed0c086f2393b9c8268

Observation 31ffbb29-d815-4946-af5d-c57eb0a8cfe3 · inbound

Accelerated Stochastic Zeroth-Order Quasar-Convex Optimization cites this paper.

Accelerated Stochastic Zeroth-Order Quasar-Convex Optimization Variance-reduced Zeroth-Order Methods for Fine-Tuning Language Models

Reference 21

Resolution
unresolved
no resolver link, observed 2026-08-01T11:19:21.586164Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-01T11:19:21.586164Z digest=sha256:9ba52a8975037f97608c5aa3690185b3ac402db201f86d434c28a253953630c8