Paper Citation Record · LEDGER

Learning a Zeroth-Order Optimizer for Fine-Tuning LLMs

As of 8 August 2026, this Paper Citation Record lists 50 of 50 outbound references and 0 inbound Pith citation observations for arXiv:2510.00419.

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

pith.paper-citation-record.v1
2510.00419 v2

Coverage vector

measured 50 of 50 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-04T13:28:50.427030Z

measured 50 of 50 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

50 of 50 outbound references displayed

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verified fuzzy0
unresolved50
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malformed identifier0
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External citation measurements

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Outbound references

Observation 69f75c68-f2aa-4619-a628-e1cc334fdaa5 · outbound

This paper cites Learning to learn by gradient descent by gradient descent.

Learning a Zeroth-Order Optimizer for Fine-Tuning LLMs Learning to learn by gradient descent by gradient descent

Reference 1

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Observation df11587f-48bf-4651-83c3-bc935e7de82b · outbound

This paper cites Qwen Technical Report.

Learning a Zeroth-Order Optimizer for Fine-Tuning LLMs Qwen Technical Report

Reference 2

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Observation 79d0f0a0-83d5-47bf-9555-ce8a1ef0d466 · outbound

This paper cites Learning to learn.

Learning a Zeroth-Order Optimizer for Fine-Tuning LLMs Learning to learn

Reference 3

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Observation 667f65c2-00ad-4710-87bf-57b933b79d27 · outbound

This paper cites A zeroth-order block coordinate descent algorithm for huge-scale black-box optimization.

Learning a Zeroth-Order Optimizer for Fine-Tuning LLMs A zeroth-order block coordinate descent algorithm for huge-scale black-box optimization

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-04T13:28:45.111653Z

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Observation 5e957725-3e3f-4388-83dd-230926e92447 · outbound

This paper cites Zoo: Zeroth order optimization based black-box attacks to deep neural networks without training substitute models.

Learning a Zeroth-Order Optimizer for Fine-Tuning LLMs Zoo: Zeroth order optimization based black-box attacks to deep neural networks without training substitute models

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-04T13:28:45.239923Z

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Observation 9dbf09f4-aac0-4dbd-b64d-fea6953263d6 · outbound

This paper cites Learning to Optimize: A Primer and A Benchmark.

Learning a Zeroth-Order Optimizer for Fine-Tuning LLMs Learning to Optimize: A Primer and A Benchmark

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-04T13:28:45.450319Z

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Observation cde177c8-cc59-4591-b6f0-d41ce197c7cd · outbound

This paper cites Enhancing Zeroth-order Fine-tuning for Language Models with Low-rank Structures.

Learning a Zeroth-Order Optimizer for Fine-Tuning LLMs Enhancing Zeroth-order Fine-tuning for Language Models with Low-rank Structures

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-04T13:28:45.687627Z

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Observation 7b931de0-0188-4d2c-97dd-dffc74f33b2b · outbound

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

Learning a Zeroth-Order Optimizer for Fine-Tuning LLMs BoolQ: Exploring the Surprising Difficulty of Natural Yes/No Questions

Reference 8

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Observation 91ebe683-d34c-47f2-a66c-23b6df382ff5 · outbound

This paper cites Cotter and P.R.

Learning a Zeroth-Order Optimizer for Fine-Tuning LLMs Cotter and P.R

Reference 9

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Observation 938017f9-c504-41e1-8057-bf46b68d5848 · outbound

This paper cites The commitmentbank: Investigating projection in naturally occurring discourse.

Learning a Zeroth-Order Optimizer for Fine-Tuning LLMs The commitmentbank: Investigating projection in naturally occurring discourse

Reference 10

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Observation 079eb5c4-7999-488c-a9e3-11726fe09f23 · outbound

This paper cites DROP: A Reading Comprehension Benchmark Requiring Discrete Reasoning Over Paragraphs.

Learning a Zeroth-Order Optimizer for Fine-Tuning LLMs DROP: A Reading Comprehension Benchmark Requiring Discrete Reasoning Over Paragraphs

Reference 11

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Observation cb4bd285-2e4f-4e10-b242-8700e9a47263 · outbound

This paper cites Generalizing gaussian smoothing for random search.

Learning a Zeroth-Order Optimizer for Fine-Tuning LLMs Generalizing gaussian smoothing for random search

Reference 12

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

This paper cites Variance-reduced Zeroth-Order Methods for Fine-Tuning Language Models.

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

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Observation 45014a56-5d7f-4084-b068-2459db7c65be · outbound

This paper cites The Llama 3 Herd of Models.

Learning a Zeroth-Order Optimizer for Fine-Tuning LLMs The Llama 3 Herd of Models

Reference 14

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Observation 4cc1d2d4-768b-478e-8a59-28115aa394b8 · outbound

This paper cites Zeroth-Order Fine-Tuning of LLMs with Extreme Sparsity.

Learning a Zeroth-Order Optimizer for Fine-Tuning LLMs Zeroth-Order Fine-Tuning of LLMs with Extreme Sparsity

Reference 15

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Observation 76c6c50a-b6c3-464f-9866-807eb3e28522 · outbound

This paper cites Zavlanos.

Learning a Zeroth-Order Optimizer for Fine-Tuning LLMs Zavlanos

Reference 16

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Observation f15492d1-15d7-4943-940f-1faf56e915ef · outbound

This paper cites Lo RA : Low-rank adaptation of large language models.

Learning a Zeroth-Order Optimizer for Fine-Tuning LLMs Lo RA : Low-rank adaptation of large language models

Reference 17

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Observation 52cd8c8c-9a12-4ecb-bff0-6148ce86b1b2 · outbound

This paper cites Zo-adamu optimizer: Adapting perturbation by the momentum and uncertainty in zeroth-order optimization, 2023.

Learning a Zeroth-Order Optimizer for Fine-Tuning LLMs Zo-adamu optimizer: Adapting perturbation by the momentum and uncertainty in zeroth-order optimization, 2023

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-04T13:28:47.074140Z

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Observation 94e0c8a3-a5fd-4e7f-b6d5-7f410b052566 · outbound

This paper cites Adam: A method for stochastic optimization.

Learning a Zeroth-Order Optimizer for Fine-Tuning LLMs Adam: A method for stochastic optimization

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-04T13:28:47.131849Z

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Observation 1fea41aa-6348-4f0d-b9fd-489e2831fdeb · outbound

This paper cites The winograd schema challenge.

Learning a Zeroth-Order Optimizer for Fine-Tuning LLMs The winograd schema challenge

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-04T13:28:47.269550Z

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Observation abb74cc3-c762-4153-baa8-d1023aca617a · outbound

This paper cites Learning to Optimize.

Learning a Zeroth-Order Optimizer for Fine-Tuning LLMs Learning to Optimize

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-04T13:28:47.347076Z

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Observation 74f78755-b27a-4131-95aa-e9507649e486 · outbound

This paper cites Learning to Optimize Neural Nets.

Learning a Zeroth-Order Optimizer for Fine-Tuning LLMs Learning to Optimize Neural Nets

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-04T13:28:47.435267Z

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Observation 25cbfa9a-635e-4e9c-9648-4f47ced0d285 · outbound

This paper cites Prefix-tuning: Optimizing continuous prompts for generation.

Learning a Zeroth-Order Optimizer for Fine-Tuning LLMs Prefix-tuning: Optimizing continuous prompts for generation

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-04T13:28:47.546754Z

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Observation fa862923-0720-44d0-a561-086310ebfe27 · outbound

This paper cites signsgd via zeroth-order oracle.

Learning a Zeroth-Order Optimizer for Fine-Tuning LLMs signsgd via zeroth-order oracle

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-04T13:28:47.712902Z

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Observation 4af8a750-ff78-4c41-8637-ecae3242eb27 · outbound

This paper cites Sparse mezo: Less parameters for better performance in zeroth-order llm fine-tuning.

Learning a Zeroth-Order Optimizer for Fine-Tuning LLMs Sparse mezo: Less parameters for better performance in zeroth-order llm fine-tuning

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-04T13:28:47.829597Z

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Observation c8445472-1811-4c4e-8eb2-ac18c1128107 · outbound

This paper cites Learning Gradient Descent: Better Generalization and Longer Horizons.

Learning a Zeroth-Order Optimizer for Fine-Tuning LLMs Learning Gradient Descent: Better Generalization and Longer Horizons

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-04T13:28:47.902903Z

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Observation 2e03bcc1-dce0-4edd-a10c-4557d0d78b7f · outbound

This paper cites Learning gradient descent: Better generalization and longer horizons.

Learning a Zeroth-Order Optimizer for Fine-Tuning LLMs Learning gradient descent: Better generalization and longer horizons

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-04T13:28:48.020105Z

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Observation 55f4754f-77a4-48a7-8d76-9f5b2fff6e32 · outbound

This paper cites Revisiting zeroth-order optimization: Minimum-variance two-point estimators and directionally aligned perturbations.

Learning a Zeroth-Order Optimizer for Fine-Tuning LLMs Revisiting zeroth-order optimization: Minimum-variance two-point estimators and directionally aligned perturbations

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-04T13:28:48.138572Z

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Observation 2a19fae9-bb0a-4745-9799-37d702e0afd1 · outbound

This paper cites Lee, Danqi Chen, and Sanjeev Arora.

Learning a Zeroth-Order Optimizer for Fine-Tuning LLMs Lee, Danqi Chen, and Sanjeev Arora

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-04T13:28:48.316032Z

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Observation 0f7fa5a5-df9e-4724-90d2-c482ab5ffab9 · outbound

This paper cites Understanding and correcting pathologies in the training of learned optimizers.

Learning a Zeroth-Order Optimizer for Fine-Tuning LLMs Understanding and correcting pathologies in the training of learned optimizers

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-04T13:28:48.406415Z

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Observation a3e4aa63-7bed-4f40-872c-ddbd1eab0d6b · outbound

This paper cites Practical tradeoffs between memory, compute, and performance in learned optimizers.

Learning a Zeroth-Order Optimizer for Fine-Tuning LLMs Practical tradeoffs between memory, compute, and performance in learned optimizers

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-04T13:28:48.569474Z

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Observation fd894578-f4a8-4d76-bebc-9954d63d4dcf · outbound

This paper cites SQuAD: 100,000+ Questions for Machine Comprehension of Text.

Learning a Zeroth-Order Optimizer for Fine-Tuning LLMs SQuAD: 100,000+ Questions for Machine Comprehension of Text

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-04T13:28:48.773965Z

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Observation d2de0fa9-da62-4925-b63a-19b286f1f493 · outbound

This paper cites Choice of plausible alternatives: An evaluation of commonsense causal reasoning.

Learning a Zeroth-Order Optimizer for Fine-Tuning LLMs Choice of plausible alternatives: An evaluation of commonsense causal reasoning

Reference 33

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Observation b0276f45-f3ee-4983-8631-24c683f96ca9 · outbound

This paper cites Learning to Learn by Zeroth-Order Oracle.

Learning a Zeroth-Order Optimizer for Fine-Tuning LLMs Learning to Learn by Zeroth-Order Oracle

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-04T13:28:49.010408Z

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Observation b78136ee-11d9-4645-af28-84670f47385e · outbound

This paper cites Hugginggraph: Understanding the supply chain of llm ecosystem.

Learning a Zeroth-Order Optimizer for Fine-Tuning LLMs Hugginggraph: Understanding the supply chain of llm ecosystem

Reference 35

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Observation 1d78d7b5-f373-460a-8be2-4bc1c03fdbd4 · outbound

This paper cites Recursive deep models for semantic compositionality over a sentiment treebank.

Learning a Zeroth-Order Optimizer for Fine-Tuning LLMs Recursive deep models for semantic compositionality over a sentiment treebank

Reference 36

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Observation 15714c68-19ad-4a56-b90b-b95a89e545e4 · outbound

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

Learning a Zeroth-Order Optimizer for Fine-Tuning LLMs TeZO: Empowering the Low-Rankness on the Temporal Dimension in the Zeroth-Order Optimization for Fine-tuning LLMs

Reference 37

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Observation c2d3b834-1053-479f-bd12-5761958b3527 · outbound

This paper cites Distributed zero-order algorithms for nonconvex multiagent optimization.

Learning a Zeroth-Order Optimizer for Fine-Tuning LLMs Distributed zero-order algorithms for nonconvex multiagent optimization

Reference 38

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Observation 3f739b37-a7b0-4ee5-ba8b-8f99e5c37188 · outbound

This paper cites Learned Optimizers that Scale and Generalize.

Learning a Zeroth-Order Optimizer for Fine-Tuning LLMs Learned Optimizers that Scale and Generalize

Reference 39

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Observation 0cd9dd7d-81eb-4d78-bb81-e9f55f9a8f2e · outbound

This paper cites Hoffman, Sergio G\' o mez Colmenarejo, Misha Denil, Nando de Freitas, and Jascha Sohl-Dickstein.

Learning a Zeroth-Order Optimizer for Fine-Tuning LLMs Hoffman, Sergio G\' o mez Colmenarejo, Misha Denil, Nando de Freitas, and Jascha Sohl-Dickstein

Reference 40

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Observation c6d0a702-ec60-45e9-b6b5-f1c53d514261 · outbound

This paper cites Hessian-Aware Zeroth-Order Optimization for Black-Box Adversarial Attack.

Learning a Zeroth-Order Optimizer for Fine-Tuning LLMs Hessian-Aware Zeroth-Order Optimization for Black-Box Adversarial Attack

Reference 41

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Observation c7d7219e-5b46-4683-afd9-2bf1a62d6399 · outbound

This paper cites Hessian-aware zeroth-order optimization for black-box adversarial attack, 2019.

Learning a Zeroth-Order Optimizer for Fine-Tuning LLMs Hessian-aware zeroth-order optimization for black-box adversarial attack, 2019

Reference 42

Resolution: unresolved; no resolver link, observed 2026-08-04T13:28:49.823785Z

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Observation 7a500683-c009-407c-adaf-29fd4c74fa38 · outbound

This paper cites OPT: Open Pre-trained Transformer Language Models.

Learning a Zeroth-Order Optimizer for Fine-Tuning LLMs OPT: Open Pre-trained Transformer Language Models

Reference 43

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Observation be4f6352-9b69-4d0b-b362-748190f90e28 · outbound

This paper cites Why transformers need adam: A hessian perspective.

Learning a Zeroth-Order Optimizer for Fine-Tuning LLMs Why transformers need adam: A hessian perspective

Reference 44

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Observation 0ecc4c2d-4554-46dd-8064-62483ceea2fa · outbound

This paper cites Adam-mini: Use Fewer Learning Rates To Gain More.

Learning a Zeroth-Order Optimizer for Fine-Tuning LLMs Adam-mini: Use Fewer Learning Rates To Gain More

Reference 45

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Observation 94119468-7bb3-413f-ab27-45a66507df1c · outbound

This paper cites Second-Order Fine-Tuning without Pain for LLMs:A Hessian Informed Zeroth-Order Optimizer.

Learning a Zeroth-Order Optimizer for Fine-Tuning LLMs Second-Order Fine-Tuning without Pain for LLMs:A Hessian Informed Zeroth-Order Optimizer

Reference 46

Resolution: unresolved; no resolver link, observed 2026-08-04T13:28:50.094513Z

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Observation 7274dcf5-8cb4-444a-bcfa-5bfe71c2d08a · outbound

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Learning a Zeroth-Order Optimizer for Fine-Tuning LLMs write newline

Reference 47

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Observation 0b13cd96-d66f-4c3e-af9e-a032c4121854 · outbound

This paper cites @esa (Ref.

Learning a Zeroth-Order Optimizer for Fine-Tuning LLMs @esa (Ref

Reference 48

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Observation a5788f91-4c4c-4582-8471-0675a735d547 · outbound

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Learning a Zeroth-Order Optimizer for Fine-Tuning LLMs Unresolved cited work

Reference 49

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Observation 46b2a5a0-7cad-4ad5-b195-44aee9453d7c · outbound

This paper cites train once, reuse widely.

Learning a Zeroth-Order Optimizer for Fine-Tuning LLMs train once, reuse widely

Reference 50

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Pith citing papers

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