Fine-Tuning Pretrained Language Models: Weight Initializations, Data Orders, and Early Stopping | Paper Citation Record

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Learning to summarize from human feedback cites this paper.

Learning to summarize from human feedback Fine-Tuning Pretrained Language Models: Weight Initializations, Data Orders, and Early Stopping

Reference 13

Resolution: verified exact; arxiv_id, observed 2026-05-18T01:46:18.549473Z

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Editing Models with Task Arithmetic cites this paper.

Editing Models with Task Arithmetic Fine-Tuning Pretrained Language Models: Weight Initializations, Data Orders, and Early Stopping

Reference 18

Resolution: metadata mismatch; arxiv_id, observed 2026-05-13T08:09:12.915111Z

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LIMO: Less is More for Reasoning cites this paper.

LIMO: Less is More for Reasoning Fine-Tuning Pretrained Language Models: Weight Initializations, Data Orders, and Early Stopping

Reference 177

Resolution: metadata mismatch; arxiv_id, observed 2026-05-17T02:11:37.389452Z

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Revisiting Bayesian Model Averaging in the Era of Foundation Models cites this paper.

Revisiting Bayesian Model Averaging in the Era of Foundation Models Fine-Tuning Pretrained Language Models: Weight Initializations, Data Orders, and Early Stopping

Reference 48

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Behavioral Augmentation of UML Class Diagrams: An Empirical Study of Large Language Models for Method Generation cites this paper.

Behavioral Augmentation of UML Class Diagrams: An Empirical Study of Large Language Models for Method Generation Fine-Tuning Pretrained Language Models: Weight Initializations, Data Orders, and Early Stopping

Reference 16

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Gradient-Based Model Fingerprinting for LLM Similarity Detection and Family Classification cites this paper.

Gradient-Based Model Fingerprinting for LLM Similarity Detection and Family Classification Fine-Tuning Pretrained Language Models: Weight Initializations, Data Orders, and Early Stopping

Reference 18

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RewardAnything: Generalizable Principle-Following Reward Models cites this paper.

RewardAnything: Generalizable Principle-Following Reward Models Fine-Tuning Pretrained Language Models: Weight Initializations, Data Orders, and Early Stopping

Reference 55

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GeistBERT: Breathing Life into German NLP cites this paper.

GeistBERT: Breathing Life into German NLP Fine-Tuning Pretrained Language Models: Weight Initializations, Data Orders, and Early Stopping

Reference 11

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Breaking a Logarithmic Barrier in the Stopping Time Convergence Rate of Stochastic First-order Methods cites this paper.

Breaking a Logarithmic Barrier in the Stopping Time Convergence Rate of Stochastic First-order Methods Fine-Tuning Pretrained Language Models: Weight Initializations, Data Orders, and Early Stopping

Reference 9

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Should We Still Pretrain Encoders with Masked Language Modeling? cites this paper.

Should We Still Pretrain Encoders with Masked Language Modeling? Fine-Tuning Pretrained Language Models: Weight Initializations, Data Orders, and Early Stopping

Reference 11

Resolution: metadata mismatch; arxiv_id, observed 2026-05-19T06:32:07.608630Z

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Observation 8ee894d9-55b1-49dd-870c-d18c85448a54 · inbound

Can Interpretation Predict Behavior on Unseen Data? cites this paper.

Can Interpretation Predict Behavior on Unseen Data? Fine-Tuning Pretrained Language Models: Weight Initializations, Data Orders, and Early Stopping

Reference 9

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Improving Data and Parameter Efficiency of Neural Language Models Using Representation Analysis cites this paper.

Improving Data and Parameter Efficiency of Neural Language Models Using Representation Analysis Fine-Tuning Pretrained Language Models: Weight Initializations, Data Orders, and Early Stopping

Reference 96

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Alignment and Safety in Large Language Models: Safety Mechanisms, Training Paradigms, and Emerging Challenges cites this paper.

Alignment and Safety in Large Language Models: Safety Mechanisms, Training Paradigms, and Emerging Challenges Fine-Tuning Pretrained Language Models: Weight Initializations, Data Orders, and Early Stopping

Reference 143

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UAV-VL-R1: Generalizing Vision-Language Models via Supervised Fine-Tuning and Multi-Stage GRPO for UAV Visual Reasoning cites this paper.

UAV-VL-R1: Generalizing Vision-Language Models via Supervised Fine-Tuning and Multi-Stage GRPO for UAV Visual Reasoning Fine-Tuning Pretrained Language Models: Weight Initializations, Data Orders, and Early Stopping

Reference 49

Resolution: metadata mismatch; arxiv_id, observed 2026-05-18T22:21:53.341085Z

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LobRA: Multi-tenant Fine-tuning over Heterogeneous Data cites this paper.

LobRA: Multi-tenant Fine-tuning over Heterogeneous Data Fine-Tuning Pretrained Language Models: Weight Initializations, Data Orders, and Early Stopping

Reference 14

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SindBERT, the Sailor: Charting the Seas of Turkish NLP cites this paper.

SindBERT, the Sailor: Charting the Seas of Turkish NLP Fine-Tuning Pretrained Language Models: Weight Initializations, Data Orders, and Early Stopping

Reference 11

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Stay Unique, Stay Efficient: Preserving Model Personality in Multi-Task Merging cites this paper.

Stay Unique, Stay Efficient: Preserving Model Personality in Multi-Task Merging Fine-Tuning Pretrained Language Models: Weight Initializations, Data Orders, and Early Stopping

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-03T19:15:52.462406Z

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In-Context Probing for Membership Inference in Fine-Tuned Language Models cites this paper.

In-Context Probing for Membership Inference in Fine-Tuned Language Models Fine-Tuning Pretrained Language Models: Weight Initializations, Data Orders, and Early Stopping

Reference 55

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Beyond Transfer Accuracy: Faithful Circuits for Controlled Low-Resource Adaptation cites this paper.

Beyond Transfer Accuracy: Faithful Circuits for Controlled Low-Resource Adaptation Fine-Tuning Pretrained Language Models: Weight Initializations, Data Orders, and Early Stopping

Reference 2023

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Robust Policy Optimization to Prevent Catastrophic Forgetting cites this paper.

Robust Policy Optimization to Prevent Catastrophic Forgetting Fine-Tuning Pretrained Language Models: Weight Initializations, Data Orders, and Early Stopping

Reference 16

Resolution: metadata mismatch; arxiv_id, observed 2026-05-16T05:37:24.444353Z

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Observation 363af6f4-f403-4ab6-98fa-81ecc4229353 · inbound

If It's Good Enough for You, It's Good Enough for Me: Transferability of Audio Sufficiencies across Models cites this paper.

If It's Good Enough for You, It's Good Enough for Me: Transferability of Audio Sufficiencies across Models Fine-Tuning Pretrained Language Models: Weight Initializations, Data Orders, and Early Stopping

Reference 49

Resolution: metadata mismatch; arxiv_id, observed 2026-05-13T18:58:08.985758Z

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Observation 38a09036-9f9e-4b69-b036-4e492a8d5e0b · inbound

Towards Adaptive Continual Model Merging via Manifold-Aware Expert Evolution cites this paper.

Towards Adaptive Continual Model Merging via Manifold-Aware Expert Evolution Fine-Tuning Pretrained Language Models: Weight Initializations, Data Orders, and Early Stopping

Reference 1

Resolution: metadata mismatch; arxiv_id, observed 2026-05-11T19:21:07.214859Z

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Dependency Parsing Across the Resource Spectrum: Evaluating Architectures on High and Low-Resource Languages cites this paper.

Dependency Parsing Across the Resource Spectrum: Evaluating Architectures on High and Low-Resource Languages Fine-Tuning Pretrained Language Models: Weight Initializations, Data Orders, and Early Stopping

Reference 38

Resolution: metadata mismatch; arxiv_id, observed 2026-05-09T05:45:21.657339Z

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Instructions Shape Production of Language, not Processing cites this paper.

Instructions Shape Production of Language, not Processing Fine-Tuning Pretrained Language Models: Weight Initializations, Data Orders, and Early Stopping

Reference 196

Resolution: verified exact; arxiv_id, observed 2026-05-13T03:12:09.663244Z

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Instructions Shape Production of Language, not Processing cites this paper.

Instructions Shape Production of Language, not Processing Fine-Tuning Pretrained Language Models: Weight Initializations, Data Orders, and Early Stopping

Reference 196

Resolution: verified exact; arxiv_id, observed 2026-05-14T21:02:58.776483Z

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Observation 7bfd5b7e-726a-4d1c-8a0d-1c10414dc513 · inbound

20/20 Vision Language Models: A Prescription for Better VLMs through Data Curation Alone cites this paper.

20/20 Vision Language Models: A Prescription for Better VLMs through Data Curation Alone Fine-Tuning Pretrained Language Models: Weight Initializations, Data Orders, and Early Stopping

Reference 13

Resolution: verified exact; arxiv_id, observed 2026-05-13T02:57:09.430005Z

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Observation e9480a13-e071-48a0-acd9-5af89844e208 · inbound

20/20 Vision Language Models: A Prescription for Better VLMs through Data Curation Alone cites this paper.

20/20 Vision Language Models: A Prescription for Better VLMs through Data Curation Alone Fine-Tuning Pretrained Language Models: Weight Initializations, Data Orders, and Early Stopping

Reference 13

Resolution: verified exact; arxiv_id, observed 2026-05-14T21:29:28.696436Z

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LoRA vs. Full Fine-Tuning: A Theoretical Perspective cites this paper.

LoRA vs. Full Fine-Tuning: A Theoretical Perspective Fine-Tuning Pretrained Language Models: Weight Initializations, Data Orders, and Early Stopping

Reference 10

Resolution: metadata mismatch; arxiv_id, observed 2026-05-20T12:18:16.801931Z

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Observation ca35d6f6-a5e9-45e3-9454-965f879c84cf · inbound

PromptRad: Knowledge-Enhanced Multi-Label Prompt-Tuning for Low-Resource Radiology Report Labeling cites this paper.

PromptRad: Knowledge-Enhanced Multi-Label Prompt-Tuning for Low-Resource Radiology Report Labeling Fine-Tuning Pretrained Language Models: Weight Initializations, Data Orders, and Early Stopping

Reference 24

Resolution: verified exact; arxiv_id, observed 2026-05-20T05:43:05.863325Z

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No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation f49a51fc-ea9f-4be6-a8c9-42960eecb63d · inbound

PromptRad: Knowledge-Enhanced Multi-Label Prompt-Tuning for Low-Resource Radiology Report Labeling cites this paper.

PromptRad: Knowledge-Enhanced Multi-Label Prompt-Tuning for Low-Resource Radiology Report Labeling Fine-Tuning Pretrained Language Models: Weight Initializations, Data Orders, and Early Stopping

Reference 24

Resolution: verified exact; arxiv_id, observed 2026-05-21T07:54:02.637557Z

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No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 9dce5377-20ef-4699-b079-4fc60b94cadd · inbound

Do Deep Networks Forget Initialization? A Forgetting-Time View of Practical Inductive Bias cites this paper.

Do Deep Networks Forget Initialization? A Forgetting-Time View of Practical Inductive Bias Fine-Tuning Pretrained Language Models: Weight Initializations, Data Orders, and Early Stopping

Reference 24

Resolution: verified exact; arxiv_id, observed 2026-06-29T13:23:28.023366Z

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No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 9bb93867-19eb-442a-8a51-673fb8b042b9 · inbound

BadBone: Backdoor Attacks Against Backbone Models in Visual Prompt Learning cites this paper.

BadBone: Backdoor Attacks Against Backbone Models in Visual Prompt Learning Fine-Tuning Pretrained Language Models: Weight Initializations, Data Orders, and Early Stopping

Reference 15

Resolution: verified exact; arxiv_id, observed 2026-07-01T19:56:11.294800Z

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PortBERT: Navigating the Depths of Portuguese Language Models cites this paper.

PortBERT: Navigating the Depths of Portuguese Language Models Fine-Tuning Pretrained Language Models: Weight Initializations, Data Orders, and Early Stopping

Reference 72

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On the Geometry of On-Policy Distillation cites this paper.

On the Geometry of On-Policy Distillation Fine-Tuning Pretrained Language Models: Weight Initializations, Data Orders, and Early Stopping

Reference 3

Resolution: metadata mismatch; arxiv_id, observed 2026-07-02T16:27:08.862860Z

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Observation 5e248862-10b4-4d88-96c3-58754aed301f · inbound

Phantom Transitions in Language Model Fine-Tuning: A Density-Matrix Analysis cites this paper.

Phantom Transitions in Language Model Fine-Tuning: A Density-Matrix Analysis Fine-Tuning Pretrained Language Models: Weight Initializations, Data Orders, and Early Stopping

Reference 10

Resolution: metadata mismatch; arxiv_id, observed 2026-06-29T22:04:00.576999Z

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Observation ebd6ee2f-ea30-48b4-850b-6e8ad4d48ac0 · inbound

Phantom Transitions in Language Model Fine-Tuning: A Density-Matrix Analysis cites this paper.

Phantom Transitions in Language Model Fine-Tuning: A Density-Matrix Analysis Fine-Tuning Pretrained Language Models: Weight Initializations, Data Orders, and Early Stopping

Reference 10

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Afrispeech Semantics: Evaluating Audio Semantic Reasoning in Spoken Language Models Across Domains and Accents cites this paper.

Afrispeech Semantics: Evaluating Audio Semantic Reasoning in Spoken Language Models Across Domains and Accents Fine-Tuning Pretrained Language Models: Weight Initializations, Data Orders, and Early Stopping

Reference 272

Resolution: verified exact; arxiv_id, observed 2026-06-30T22:15:05.450842Z

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Observation 184ff01d-adbf-4fc1-a310-130609772a5f · inbound

Sparsity Curse: Understanding RLVR Model Parameter Space from Model Merging cites this paper.

Sparsity Curse: Understanding RLVR Model Parameter Space from Model Merging Fine-Tuning Pretrained Language Models: Weight Initializations, Data Orders, and Early Stopping

Reference 5

Resolution: verified exact; arxiv_id, observed 2026-07-03T21:08:57.821995Z

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Observation e927e833-4491-4ed6-961a-358ea600a34d · inbound

MiqraBERT: Regression-Based Sentence-BERT Finetuning for Biblical Hebrew Parallel Detection cites this paper.

MiqraBERT: Regression-Based Sentence-BERT Finetuning for Biblical Hebrew Parallel Detection Fine-Tuning Pretrained Language Models: Weight Initializations, Data Orders, and Early Stopping

Reference 4

Resolution: metadata mismatch; arxiv_id, observed 2026-07-04T01:19:20.994944Z

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Observation 461299ca-31ab-42f5-9b20-daf5450a4ce1 · inbound

MiqraBERT: Regression-Based Sentence-BERT Finetuning for Biblical Hebrew Parallel Detection cites this paper.

MiqraBERT: Regression-Based Sentence-BERT Finetuning for Biblical Hebrew Parallel Detection Fine-Tuning Pretrained Language Models: Weight Initializations, Data Orders, and Early Stopping

Reference 5

Resolution: metadata mismatch; arxiv_id, observed 2026-06-26T20:49:57.860299Z

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No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 014aceff-902a-4c32-b1a8-af77508d665f · inbound

Repository-Level Solidity Code Generation with Large Language Models: From Prompting to Fine-Tuning cites this paper.

Repository-Level Solidity Code Generation with Large Language Models: From Prompting to Fine-Tuning Fine-Tuning Pretrained Language Models: Weight Initializations, Data Orders, and Early Stopping

Reference 13

Resolution: verified exact; arxiv_id, observed 2026-07-04T04:49:34.445786Z

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No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 4cef2d21-e320-4d4b-863c-71bb436ec705 · inbound

The FID Lottery: Quantifying Hidden Randomness in Generative-Model Evaluation cites this paper.

The FID Lottery: Quantifying Hidden Randomness in Generative-Model Evaluation Fine-Tuning Pretrained Language Models: Weight Initializations, Data Orders, and Early Stopping

Reference 22

Resolution: verified exact; arxiv_id, observed 2026-07-04T03:49:29.528251Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 2be36366-81bd-4a57-acdb-4dbf56f663f0 · inbound

Sub-Billion, Super-Frontier: Small Language Models Rival Zero-Shot Frontier LLMs on General and Literary Relation Extraction cites this paper.

Sub-Billion, Super-Frontier: Small Language Models Rival Zero-Shot Frontier LLMs on General and Literary Relation Extraction Fine-Tuning Pretrained Language Models: Weight Initializations, Data Orders, and Early Stopping

Reference 249

Resolution: verified exact; arxiv_id, observed 2026-07-04T09:19:42.859218Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 75c89c94-3105-427c-ab92-09316fecc380 · inbound

GRAIN: Group Aggregation via Min-Norm Objective cites this paper.

GRAIN: Group Aggregation via Min-Norm Objective Fine-Tuning Pretrained Language Models: Weight Initializations, Data Orders, and Early Stopping

Reference 72

Resolution: metadata mismatch; arxiv_id, observed 2026-07-04T09:59:45.269882Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 8039ae6c-1868-4493-9144-5659599b240b · inbound

Optimizer Memory Makes Shuffle Order a First-Order Source of Fine-Tuning Noise cites this paper.

Optimizer Memory Makes Shuffle Order a First-Order Source of Fine-Tuning Noise Fine-Tuning Pretrained Language Models: Weight Initializations, Data Orders, and Early Stopping

Reference 40

Resolution: metadata mismatch; arxiv_id, observed 2026-06-30T07:24:21.865183Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 3cfef4e6-f123-47ec-990d-3c71ffae3f54 · inbound

Training Large Language Models for Self-Explanation Faithfulness cites this paper.

Training Large Language Models for Self-Explanation Faithfulness Fine-Tuning Pretrained Language Models: Weight Initializations, Data Orders, and Early Stopping

Reference 49

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Observation f0d39616-0efc-41c9-9614-13a0b18ab9e9 · inbound

Latent-LoRA: Compact Latent-Space Adapters with Gradient-Free Routing for Continual Learning cites this paper.

Latent-LoRA: Compact Latent-Space Adapters with Gradient-Free Routing for Continual Learning Fine-Tuning Pretrained Language Models: Weight Initializations, Data Orders, and Early Stopping

Reference 46

Resolution: unresolved; no resolver link, observed 2026-07-30T10:55:15.500484Z

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Observation e787555d-0ada-4d70-89b4-998b2c209f89 · inbound

What We Observe as LLM Behavior Can Be a Side-effect of Inference Backend cites this paper.

What We Observe as LLM Behavior Can Be a Side-effect of Inference Backend Fine-Tuning Pretrained Language Models: Weight Initializations, Data Orders, and Early Stopping

Reference 2020

Resolution: unresolved; no resolver link, observed 2026-08-06T18:22:28.042785Z

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