Paper Citation Record · LEDGER

PLoP: Precise LoRA Placement for Efficient Finetuning of Large Models

As of 7 August 2026, this Paper Citation Record lists 42 of 42 outbound references and 3 inbound Pith citation observations for arXiv:2506.20629.

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

pith.paper-citation-record.v1
2506.20629 v1

Coverage vector

measured 42 of 42 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T22:50:15.749239Z

measured 45 of 45 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-06T06:34:29.942622+00:00

measured 3 of 3 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-02T05:16:35.355130Z

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

42 of 42 outbound references displayed

verified exact2
verified fuzzy12
unresolved27
parse uncertain0
malformed identifier1
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation b5db60fe-41bd-4e44-9518-a79662231a5a · outbound

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

PLoP: Precise LoRA Placement for Efficient Finetuning of Large Models Lora: Low-rank adaptation of large language models

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-06T22:50:11.762337Z

Source-reported events for the cited work

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Observation bc146791-03eb-470b-ba9f-97bd0c413541 · outbound

This paper cites LoRA+: Efficient low rank adaptation of large models.

PLoP: Precise LoRA Placement for Efficient Finetuning of Large Models LoRA+: Efficient low rank adaptation of large models

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:50:18.645387Z

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No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation d6846caf-cf64-4ca6-90dd-ea8b981b5217 · outbound

This paper cites Dora: Weight-decomposed low-rank adaptation.

PLoP: Precise LoRA Placement for Efficient Finetuning of Large Models Dora: Weight-decomposed low-rank adaptation

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:50:18.456925Z

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No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation afdf1c00-ad44-45e4-ae95-dd2109535032 · outbound

This paper cites RA-LoRA: Rank- adaptive parameter-efficient fine-tuning for accurate 2-bit quantized large language models.

PLoP: Precise LoRA Placement for Efficient Finetuning of Large Models RA-LoRA: Rank- adaptive parameter-efficient fine-tuning for accurate 2-bit quantized large language models

Reference 4

Resolution: malformed identifier; no resolver link, observed 2026-08-06T22:50:11.916739Z

Source-reported events for the cited work

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Observation 4f14b611-5324-426a-9f6a-8c236fabb752 · outbound

This paper cites Take Only What You Need: Rank Minimization as an Implicit Forgetting Regularizer in Continual Learning.

PLoP: Precise LoRA Placement for Efficient Finetuning of Large Models Take Only What You Need: Rank Minimization as an Implicit Forgetting Regularizer in Continual Learning

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-06T22:50:11.955707Z

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Observation 411880e2-f4d0-4922-8149-838dee2eefcb · outbound

This paper cites The impact of initialization on lora finetuning dynamics.

PLoP: Precise LoRA Placement for Efficient Finetuning of Large Models The impact of initialization on lora finetuning dynamics

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:50:18.283837Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 32f10d84-83ea-4ff9-8bb6-cbc0cb9fd109 · outbound

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

PLoP: Precise LoRA Placement for Efficient Finetuning of Large Models AdaLoRA: Adaptive Budget Allocation for Parameter-Efficient Fine-Tuning

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-06T22:50:12.149846Z

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Observation dad788d7-cbe3-45db-ab3d-6b88285e0e49 · outbound

This paper cites Qlora: Effi- cient finetuning of quantized llms.

PLoP: Precise LoRA Placement for Efficient Finetuning of Large Models Qlora: Effi- cient finetuning of quantized llms

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:50:17.926630Z

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No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 60eda5be-c04f-4910-a234-c24fe22a6fcd · outbound

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

PLoP: Precise LoRA Placement for Efficient Finetuning of Large Models VeRA: Vector-based Random Matrix Adaptation

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-06T22:50:12.267784Z

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Observation 5c6837c7-1911-4314-905f-1ef8bbbf7e3a · outbound

This paper cites LoRA-FA: Efficient and Effective Low Rank Representation Fine-tuning.

PLoP: Precise LoRA Placement for Efficient Finetuning of Large Models LoRA-FA: Efficient and Effective Low Rank Representation Fine-tuning

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-06T22:50:12.331356Z

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Observation ee0ed6d0-85d7-49d5-b489-a2954f848beb · outbound

This paper cites HydraLoRA: An Asymmetric LoRA Architecture for Efficient Fine-Tuning.

PLoP: Precise LoRA Placement for Efficient Finetuning of Large Models HydraLoRA: An Asymmetric LoRA Architecture for Efficient Fine-Tuning

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-06T22:50:12.357927Z

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Observation 6d5a7cc4-248e-4a91-8ea7-52f11502b57a · outbound

This paper cites MoRA: High-Rank Updating for Parameter-Efficient Fine-Tuning.

PLoP: Precise LoRA Placement for Efficient Finetuning of Large Models MoRA: High-Rank Updating for Parameter-Efficient Fine-Tuning

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-06T22:50:12.408587Z

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Observation f9d36fd4-5867-4440-8523-58922a1ec4a0 · outbound

This paper cites A Note on LoRA.

PLoP: Precise LoRA Placement for Efficient Finetuning of Large Models A Note on LoRA

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-06T22:50:12.474879Z

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Observation 9cd9a7cb-07f0-49eb-bc91-7b730ad0733e · outbound

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

PLoP: Precise LoRA Placement for Efficient Finetuning of Large Models Towards a Unified View of Parameter-Efficient Transfer Learning

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-06T22:50:12.504083Z

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Observation 383b47b1-d223-4a68-a1ec-16c743680418 · outbound

This paper cites The llama 3 herd of models, 2024.

PLoP: Precise LoRA Placement for Efficient Finetuning of Large Models The llama 3 herd of models, 2024

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:50:17.756774Z

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No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 6102ceff-bbf2-45f1-8b94-602a10a7f861 · outbound

This paper cites Cross-Attention is All You Need: Adapting Pretrained Transformers for Machine Translation.

PLoP: Precise LoRA Placement for Efficient Finetuning of Large Models Cross-Attention is All You Need: Adapting Pretrained Transformers for Machine Translation

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-06T22:50:12.706998Z

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Observation cd0794ff-9d5f-4fb4-be95-65d80cded92d · outbound

This paper cites Gradient-based parameter selection for efficient fine-tuning.

PLoP: Precise LoRA Placement for Efficient Finetuning of Large Models Gradient-based parameter selection for efficient fine-tuning

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:50:17.562213Z

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No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 2da089bd-1adc-4d98-bc7a-aa29fc568acd · outbound

This paper cites Sensitivity-aware visual parameter-efficient fine-tuning.

PLoP: Precise LoRA Placement for Efficient Finetuning of Large Models Sensitivity-aware visual parameter-efficient fine-tuning

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:50:17.380197Z

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No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation ce9e4aa4-272e-4d60-b1a5-fefd85e3a4f3 · outbound

This paper cites an unresolved cited work.

PLoP: Precise LoRA Placement for Efficient Finetuning of Large Models Unresolved cited work

Reference 19

Resolution: unresolved; raw_fallback, observed 2026-08-06T22:50:17.238859Z

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No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation ce83a540-3168-4779-9db0-909cbe759f6d · outbound

This paper cites Kingma and Jimmy Ba.

PLoP: Precise LoRA Placement for Efficient Finetuning of Large Models Kingma and Jimmy Ba

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:50:17.096221Z

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No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 8dc6addd-a51a-4ece-bbfc-5d095e255263 · outbound

This paper cites signSGD: Compressed Optimisation for Non-Convex Problems.

PLoP: Precise LoRA Placement for Efficient Finetuning of Large Models signSGD: Compressed Optimisation for Non-Convex Problems

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-06T22:50:13.453468Z

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Observation dd1a8385-e7d5-48c7-a0fd-26dbbedfddfe · outbound

This paper cites Visualising feature learning in deep neural networks by diagonalizing the forward feature map, 2024.

PLoP: Precise LoRA Placement for Efficient Finetuning of Large Models Visualising feature learning in deep neural networks by diagonalizing the forward feature map, 2024

Reference 22

Resolution: verified exact; raw_fallback, observed 2026-08-06T22:50:16.273915Z

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No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 516add5c-cd9c-420e-80ee-e4bf65044507 · outbound

This paper cites Implicit Regularization via Neural Feature Alignment.

PLoP: Precise LoRA Placement for Efficient Finetuning of Large Models Implicit Regularization via Neural Feature Alignment

Reference 23

Resolution: verified exact; local_arxiv, observed 2026-08-06T22:50:16.027006Z

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No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 75d14942-7f09-45ca-95e0-f31ddb2a1c04 · outbound

This paper cites Feature learning and signal propagation in deep neural networks.

PLoP: Precise LoRA Placement for Efficient Finetuning of Large Models Feature learning and signal propagation in deep neural networks

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:50:16.966923Z

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No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 18e2395b-0685-4b1d-bddd-cbc855c1caa0 · outbound

This paper cites Understanding and Minimising Outlier Features in Neural Network Training.

PLoP: Precise LoRA Placement for Efficient Finetuning of Large Models Understanding and Minimising Outlier Features in Neural Network Training

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-06T22:50:13.820346Z

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Observation a23e82c6-11ae-4b8d-9364-b6de3b3eef0b · outbound

This paper cites Training Verifiers to Solve Math Word Problems.

PLoP: Precise LoRA Placement for Efficient Finetuning of Large Models Training Verifiers to Solve Math Word Problems

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-06T22:50:13.890990Z

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Observation 5183aeeb-7db3-4afc-abd7-7ba021bb41d9 · outbound

This paper cites Evaluating Large Language Models Trained on Code.

PLoP: Precise LoRA Placement for Efficient Finetuning of Large Models Evaluating Large Language Models Trained on Code

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-06T22:50:13.961350Z

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Observation 447d596a-cfef-43ab-8d04-ea5ccab57416 · outbound

This paper cites Measuring Massive Multitask Language Understanding.

PLoP: Precise LoRA Placement for Efficient Finetuning of Large Models Measuring Massive Multitask Language Understanding

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-06T22:50:14.042709Z

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Observation b3b6997b-fa6a-4013-b85e-751b70d60b89 · outbound

This paper cites DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models.

PLoP: Precise LoRA Placement for Efficient Finetuning of Large Models DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-06T22:50:14.160586Z

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Observation eb7b65fa-b8be-4540-86d1-5ccfe1ac932e · outbound

This paper cites Qwen3 technical report, April 2025.

PLoP: Precise LoRA Placement for Efficient Finetuning of Large Models Qwen3 technical report, April 2025

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:50:16.813184Z

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No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 7e6ccd7b-320c-4252-ac65-c033859c8043 · outbound

This paper cites Gemma 3 technical report, 2025.

PLoP: Precise LoRA Placement for Efficient Finetuning of Large Models Gemma 3 technical report, 2025

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-06T22:50:14.336198Z

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Observation 5db56f86-e897-44f6-9275-bec66881314d · outbound

This paper cites Adversarial nli: A new benchmark for natural language understanding.

PLoP: Precise LoRA Placement for Efficient Finetuning of Large Models Adversarial nli: A new benchmark for natural language understanding

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:50:16.682332Z

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No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 1f47f458-6a09-4c7a-af59-185ffdbe48e0 · outbound

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

PLoP: Precise LoRA Placement for Efficient Finetuning of Large Models MetaMath: Bootstrap Your Own Mathematical Questions for Large Language Models

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-06T22:50:14.482792Z

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Observation e070fe5c-7e87-41b8-8a10-5bf39df4fad6 · outbound

This paper cites Monte Carlo Tree Search Boosts Reasoning via Iterative Preference Learning.

PLoP: Precise LoRA Placement for Efficient Finetuning of Large Models Monte Carlo Tree Search Boosts Reasoning via Iterative Preference Learning

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-06T22:50:14.576341Z

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Observation 1be3cd60-22ba-4da2-b15d-2d4d881126db · outbound

This paper cites DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning.

PLoP: Precise LoRA Placement for Efficient Finetuning of Large Models DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-06T22:50:14.684254Z

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Observation f0915151-274c-4caf-ba34-072f6be67ad8 · outbound

This paper cites Rae, Oriol Vinyals, and Laurent Sifre.

PLoP: Precise LoRA Placement for Efficient Finetuning of Large Models Rae, Oriol Vinyals, and Laurent Sifre

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-06T22:50:14.881189Z

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Observation e33a345d-cbf0-45e2-bc08-32823397018e · outbound

This paper cites Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification.

PLoP: Precise LoRA Placement for Efficient Finetuning of Large Models Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-06T22:50:15.138441Z

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Unavailable: canonical work link unavailable.

Observation 885b4b84-f9cf-4666-89b9-1eddf68da088 · outbound

This paper cites On the impact of the activation function on deep neural networks training.

PLoP: Precise LoRA Placement for Efficient Finetuning of Large Models On the impact of the activation function on deep neural networks training

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:50:16.534788Z

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No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 182b4bf2-086e-478b-a7e5-4997fd1f11a4 · outbound

This paper cites Scaling Limits of Wide Neural Networks with Weight Sharing: Gaussian Process Behavior, Gradient Independence, and Neural Tangent Kernel Derivation.

PLoP: Precise LoRA Placement for Efficient Finetuning of Large Models Scaling Limits of Wide Neural Networks with Weight Sharing: Gaussian Process Behavior, Gradient Independence, and Neural Tangent Kernel Derivation

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-06T22:50:15.749239Z

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Unavailable: canonical work link unavailable.

Observation a332630d-d8ed-4262-a49f-6f32e235eb1e · outbound

This paper cites Adam: A Method for Stochastic Optimization.

PLoP: Precise LoRA Placement for Efficient Finetuning of Large Models Adam: A Method for Stochastic Optimization

Reference 2017

Resolution: unresolved; no resolver link, observed 2026-08-06T22:50:13.370576Z

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Observation 6b15f366-c0a1-41dd-8aa3-21ed137496a3 · outbound

This paper cites Feature Learning in Infinite-Width Neural Networks.

PLoP: Precise LoRA Placement for Efficient Finetuning of Large Models Feature Learning in Infinite-Width Neural Networks

Reference 2022

Resolution: unresolved; no resolver link, observed 2026-08-06T22:50:13.117590Z

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Observation 8decc2cc-64c6-4c65-89a0-bc6e92d4e8e4 · outbound

This paper cites an unresolved cited work.

PLoP: Precise LoRA Placement for Efficient Finetuning of Large Models Unresolved cited work

Reference 2024

Resolution: unresolved; raw_fallback, observed 2026-08-06T22:50:18.095224Z

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No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Pith citing papers

Observation b39c9925-8b32-4b65-8f19-cfb6d7b58af3 · inbound

Data-Efficient Adaptation of LLMs via Attention Head Reweighting cites this paper.

Data-Efficient Adaptation of LLMs via Attention Head Reweighting PLoP: Precise LoRA Placement for Efficient Finetuning of Large Models

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-02T05:16:35.355130Z

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Observation a91d7e92-debe-4d2e-9bb7-a94f201777d4 · inbound

Non-vacuous Generalization Bounds for Reinforcement Learning with Verifiable Rewards cites this paper.

Non-vacuous Generalization Bounds for Reinforcement Learning with Verifiable Rewards PLoP: Precise LoRA Placement for Efficient Finetuning of Large Models

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-02T02:01:50.780452Z

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Observation ab7739c6-94ab-4006-b8c5-da9c222a1270 · inbound

PoLoRA: A Preconditioned Orthogonalized LoRA Optimizer cites this paper.

PoLoRA: A Preconditioned Orthogonalized LoRA Optimizer PLoP: Precise LoRA Placement for Efficient Finetuning of Large Models

Reference 2025

Resolution: unresolved; no resolver link, observed 2026-08-01T17:32:33.530247Z

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Unavailable: canonical work link unavailable.