Aussie AI

Fine Tuning

Last Updated 27 August, 2025

by David Spuler, Ph.D.

Research on Fine Tuning

Research papers include:

Libo Qin, Qiguang Chen, Xiachong Feng, Yang Wu, Yongheng Zhang, Yinghui Li, Min Li, Wanxiang Che, Philip S. Yu, 21 May 2024, Large Language Models Meet NLP: A Survey, https://arxiv.org/abs/2405.12819 (A survey of research into how LLMs, with and without fine-tuning, perform in various NLP use cases, such as mathematical reasoning, dialogue understanding, translation, and more.)
Runheng Liu, Xingchen Xiao, Heyan Huang, Zewen Chi, Zhijing Wu, 7 May 2024, FlashBack:Efficient Retrieval-Augmented Language Modeling for Long Context Inference, https://arxiv.org/abs/2405.04065 (Optimize RAG by appending rather than prepending documents, and modifying the attention for improvements in KV caching, by shimming or replacing some of the CUDA GPU low-level memory management APIs to avoid the need to rewrite kernels with extra higher-level memory management code.)
Benjue Weng, 13 Apr 2024, Navigating the Landscape of Large Language Models: A Comprehensive Review and Analysis of Paradigms and Fine-Tuning Strategies, https://arxiv.org/abs/2404.09022 (Reviewing fine-tuning of large models.)
Tal Peretz, 15 NOV 2023, The Developer's Guide to Production-Grade LLM Apps: Advanced Techniques for Maximizing LLM Performance, https://buildingaistuff.com/p/the-developers-guide-to-production
Haoran Xu, Amr Sharaf, Yunmo Chen, Weiting Tan, Lingfeng Shen, Benjamin Van Durme, Kenton Murray, Young Jin Kim, 18 Jan 2024, Contrastive Preference Optimization: Pushing the Boundaries of LLM Performance in Machine Translation, https://arxiv.org/abs/2401.08417
David Spuler, March 2024, Chapter 6. Training, Fine-Tuning & RAG, Generative AI in C++: Coding Transformers and LLMs, https://www.amazon.com/dp/B0CXJKCWX9
Mengwei Xu, Wangsong Yin, Dongqi Cai, Rongjie Yi, Daliang Xu, Qipeng Wang, Bingyang Wu, Yihao Zhao, Chen Yang, Shihe Wang, Qiyang Zhang, Zhenyan Lu, Li Zhang, Shangguang Wang, Yuanchun Li, Yunxin Liu, Xin Jin, Xuanzhe Liu, 16 Jan 2024, A Survey of Resource-efficient LLM and Multimodal Foundation Models, https://arxiv.org/abs/2401.08092 Project: https://github.com/UbiquitousLearning/Efficient_Foundation_Model_Survey
kipply's blog, 2023-03-30, Transformer Taxonomy (the last lit review), https://kipp.ly/transformer-taxonomy/ (Papers for all the Transformer architectures and milestone papers for the major optimization improvements on them.)
Pranav Patel, 2024, In-depth guide to fine-tuning LLMs with LoRA and QLoRA, https://www.mercity.ai/blog-post/guide-to-fine-tuning-llms-with-lora-and-qlora
Kai Lv, Yuqing Yang, Tengxiao Liu, Qinghui Gao, Qipeng Guo, Xipeng Qiu, 6 Jun 2024 (v2), Full Parameter Fine-tuning for Large Language Models with Limited Resources, https://arxiv.org/abs/2306.09782 Code: https://github.com/OpenLMLab/LOMO (Low-memory usage for full-parameter fine-tuning.)
Louis-François Bouchard, Louie Peters, May 2024, Chapter 10: Fine-Tuning, Building LLMs for Production: Enhancing LLM Abilities and Reliability with Prompting, Fine-Tuning, and RAG, https://www.amazon.com/Building-LLMs-Production-Reliability-Fine-Tuning/dp/B0D4FFPFW8/
Valentina Alto, 2024, Chapter 11: Fine-Tuning Large Language Models, Building LLM-Powered Applications: Create intelligence apps and agents with large language models, Packt Publishing, https://www.amazon.com/Building-LLM-Apps-Intelligent-Language/dp/1835462316/
Aarushi Kansal, Chapter 5: Fine-Tuning: The Theory, Chapter 6: Fine-Tuning: Hands-On,, Building Generative AI-Powered Apps: A Hands-on Guide for Developers, Apress, https://www.amazon.com/Building-Generative-AI-Powered-Apps-Hands-ebook/dp/B0CTXXP1S4/
Xinji Mai, Zeng Tao, Junxiong Lin, Haoran Wang, Yang Chang, Yanlan Kang, Yan Wang, Wenqiang Zhang, 27 Jun 2024, From Efficient Multimodal Models to World Models: A Survey, https://arxiv.org/abs/2407.00118 (A survey of multimodal models with coverage of many optimization techniques.)
Yi Zhou, Dec 16, 2023, Optimizing GenAI: Comparing Model Training, Fine-Tuning, RAG, and Prompt Engineering, https://medium.com/generative-ai-revolution-ai-native-transformation/optimizing-genai-comparing-model-training-fine-tuning-rag-and-prompt-engineering-7a7c6c65e0f0
Dan Peng, Zhihui Fu, Jun Wang, 1 Jul 2024, PocketLLM: Enabling On-Device Fine-Tuning for Personalized LLMs, https://arxiv.org/abs/2407.01031 (Running fine-tuning on a smartphone via a low-memory optimization using a "derivative-free" "zeroth-order" technique called MeZo, with advantages such as privacy.)
OpenAI, August 20, 2024, Fine-tuning now available for GPT-4o, https://openai.com/index/gpt-4o-fine-tuning/
Judy Hanwen Shen, Inioluwa Deborah Raji, Irene Y. Chen, 8 Aug 2024, The Data Addition Dilemma, https://arxiv.org/abs/2408.04154
Yihua Zhang, Pingzhi Li, Junyuan Hong, Jiaxiang Li, Yimeng Zhang, Wenqing Zheng, Pin-Yu Chen, Jason D. Lee, Wotao Yin, Mingyi Hong, Zhangyang Wang, Sijia Liu, Tianlong Chen, 28 May 2024 (v3) Revisiting Zeroth-Order Optimization for Memory-Efficient LLM Fine-Tuning: A Benchmark, https://arxiv.org/abs/2402.11592 Code: https://github.com/ZO-Bench/ZO-LLM
Junjie Ye, Yuming Yang, Qi Zhang, Tao Gui, Xuanjing Huang, Peng Wang, Zhongchao Shi, Jianping Fan, 24 Sep 2024, Empirical Insights on Fine-Tuning Large Language Models for Question-Answering, https://arxiv.org/abs/2409.15825
Foundry AI, Oct 2024, When Should You Move Beyond Prompting and Start Fine-Tuning? https://thefoundryai.com/blog/fine-tuning
Siyun Zhao, Yuqing Yang, Zilong Wang, Zhiyuan He, Luna K. Qiu, Lili Qiu, 23 Sep 2024, Retrieval Augmented Generation (RAG) and Beyond: A Comprehensive Survey on How to Make your LLMs use External Data More Wisely, https://arxiv.org/abs/2409.14924
Venkatesh Balavadhani Parthasarathy, Ahtsham Zafar, Aafaq Khan, Arsalan Shahid, 30 Oct 2024 (v3), The Ultimate Guide to Fine-Tuning LLMs from Basics to Breakthroughs: An Exhaustive Review of Technologies, Research, Best Practices, Applied Research Challenges and Opportunities, https://arxiv.org/abs/2408.13296
Angels Balaguer, Vinamra Benara, Renato Luiz de Freitas Cunha, Roberto de M. Estevão Filho, Todd Hendry, Daniel Holstein, Jennifer Marsman, Nick Mecklenburg, Sara Malvar, Leonardo O. Nunes, Rafael Padilha, Morris Sharp, Bruno Silva, Swati Sharma, Vijay Aski, Ranveer Chandra, 30 Jan 2024 (v3), RAG vs Fine-tuning: Pipelines, Tradeoffs, and a Case Study on Agriculture, https://arxiv.org/abs/2401.08406
Towards AI, December 24, 2024, Llm Fine Tuning Guide: Do You Need It and How to Do It https://towardsai.net/p/artificial-intelligence/llm-fine-tuning-guide-do-you-need-it-and-how-to-do-it-4
Andrea Matarazzo, Riccardo Torlone, 3 Jan 2025, A Survey on Large Language Models with some Insights on their Capabilities and Limitations, https://arxiv.org/abs/2501.04040 (Broad survey with many LLM topics covered from history to architectures to optimizations.)
Tong Xiao, Jingbo Zhu, 16 Jan 2025, Foundations of Large Language Models, https://arxiv.org/abs/2501.09223 (Huge 230 page paper on many topics such as training, prompting, alignment, and long context.)
Guiyao Tie, Zeli Zhao, Dingjie Song, Fuyang Wei, Rong Zhou, Yurou Dai, Wen Yin, Zhejian Yang, Jiangyue Yan, Yao Su, Zhenhan Dai, Yifeng Xie, Yihan Cao, Lichao Sun, Pan Zhou, Lifang He, Hechang Chen, Yu Zhang, Qingsong Wen, Tianming Liu, Neil Zhenqiang Gong, Jiliang Tang, Caiming Xiong, Heng Ji, Philip S. Yu, Jianfeng Gao, 8 Mar 2025, A Survey on Post-training of Large Language Models, https://arxiv.org/abs/2503.06072
Maxime Heuillet, Yufei Cui, Boxing Chen, Audrey Durand, Prasanna Parthasarathi, 13 Aug 2025, Nested-ReFT: Efficient Reinforcement Learning for Large Language Model Fine-Tuning via Off-Policy Rollouts, https://arxiv.org/abs/2508.10123
Tianjun Yuan, Jiaxiang Geng, Pengchao Han, Xianhao Chen, Bing Luo, 14 Aug 2025, Flexible Personalized Split Federated Learning for On-Device Fine-Tuning of Foundation Models, https://arxiv.org/abs/2508.10349
Dongyue Li and Hongyang R. Zhang, 13 Aug 2025, Improved Regularization and Robustness for Fine-tuning in Neural Networks, https://arxiv.org/abs/2111.04578
Yanxia Deng, Aozhong Zhang, Selcuk Gurses, Naigang Wang, Zi Yang and Penghang Yin, 14 Aug 2025, CLoQ: Enhancing Fine-Tuning of Quantized LLMs via Calibrated LoRA Initialization, https://arxiv.org/abs/2501.18475
Suhas G Hegde, Shilpy Kaur, Aruna Tiwari, 14 Aug 2025, VectorFit : Adaptive Singular & Bias Vector Fine-Tuning of Pre-trained Foundation Models, https://arxiv.org/abs/2503.19530
Andrew P. Berg, Qian Zhang, Mia Y. Wang, 14 Aug 2025, 15,500 Seconds: Lean UAV Classification Using EfficientNet and Lightweight Fine-Tuning, https://arxiv.org/abs/2506.11049
Sol\`ene Debuys\`ere, Nicolas Trouv\'e, Nathan Letheule, Olivier L\'ev\^eque, Elise Colin, 14 Aug 2025, Quantitative Comparison of Fine-Tuning Techniques for Pretrained Latent Diffusion Models in the Generation of Unseen SAR Images, https://arxiv.org/abs/2506.13307
Gabriel J. Perin, Runjin Chen, Xuxi Chen, Nina S. T. Hirata, Zhangyang Wang, Junyuan Hong, 23 Jul 2025, LoX: Low-Rank Extrapolation Robustifies LLM Safety Against Fine-tuning, https://arxiv.org/abs/2506.15606
Simon Ouellette, 17 Jul 2025, Out-of-Distribution Generalization in the ARC-AGI Domain: Comparing Execution-Guided Neural Program Synthesis and Test-Time Fine-Tuning, https://arxiv.org/abs/2507.15877
Boheng Li, Renjie Gu, Junjie Wang, Leyi Qi, Yiming Li, Run Wang, Zhan Qin, Tianwei Zhang, 22 Jul 2025, Towards Resilient Safety-driven Unlearning for Diffusion Models against Downstream Fine-tuning, https://arxiv.org/abs/2507.16302
Helena Casademunt, Caden Juang, Adam Karvonen, Samuel Marks, Senthooran Rajamanoharan, Neel Nanda, 22 Jul 2025, Steering Out-of-Distribution Generalization with Concept Ablation Fine-Tuning, https://arxiv.org/abs/2507.16795
Ao Shen, Qiang Wang, Zhiquan Lai, Xionglve Li, Dongsheng Li, 22 Jul 2025, Accurate and Efficient Fine-Tuning of Quantized Large Language Models Through Optimal Balance, https://arxiv.org/abs/2407.17029
Furong Peng, Jinzhen Gao, Xuan Lu, Kang Liu, Yifan Huo, Sheng Wang, 22 Jul 2025, Towards a deeper GCN: Alleviate over-smoothing with iterative training and fine-tuning, https://arxiv.org/abs/2506.17576
Binghua Li, Ziqing Chang, Tong Liang, Chao Li, Toshihisa Tanaka, Shigeki Aoki, Qibin Zhao, Zhe Sun, 24 Jul 2025, Parameter-Efficient Fine-Tuning of 3D DDPM for MRI Image Generation Using Tensor Networks, https://arxiv.org/abs/2507.18112
Ziming Yu, Pan Zhou, Sike Wang, Jia Li, Mi Tian, Hua Huang, 24 Jul 2025, Zeroth-Order Fine-Tuning of LLMs in Random Subspaces, https://arxiv.org/abs/2410.08989
Tim Rensmeyer, Denis Kramer, Oliver Niggemann, 18 Jul 2025, On-the-Fly Fine-Tuning of Foundational Neural Network Potentials: A Bayesian Neural Network Approach, https://arxiv.org/abs/2507.13805
Amro Abdalla, Ismail Shaheen, Dan DeGenaro, Rupayan Mallick, Bogdan Raita, Sarah Adel Bargal, 18 Jul 2025, GIFT: Gradient-aware Immunization of diffusion models against malicious Fine-Tuning with safe concepts retention, https://arxiv.org/abs/2507.13598
Rafiq Kamel, Filippo Guerranti, Simon Geisler, Stephan G\"unnemann, 15 Jul 2025, SAFT: Structure-Aware Fine-Tuning of LLMs for AMR-to-Text Generation, https://arxiv.org/abs/2507.13381
Qitao Tan, Jun Liu, Zheng Zhan, Caiwei Ding, Yanzhi Wang, Xiaolong Ma, Jaewoo Lee, Jin Lu, Geng Yuan, 18 Jul 2025, Harmony in Divergence: Towards Fast, Accurate, and Memory-efficient Zeroth-order LLM Fine-tuning, https://arxiv.org/abs/2502.03304
Harsh Nilesh Pathak and Randy Paffenroth, 18 Jul 2025, Solo Connection: A Parameter Efficient Fine-Tuning Technique for Transformers, https://arxiv.org/abs/2507.14353
Fufang Wen and Shichang Zhang, 14 Jul 2025, Retention analysis of edited knowledge after fine-tuning, https://arxiv.org/abs/2507.14198
Yujia Tong, Jingling Yuan, Tian Zhang, Jianquan Liu, Chuang Hu, 19 Jul 2025, DFQ-ViT: Data-Free Quantization for Vision Transformers without Fine-tuning, https://arxiv.org/abs/2507.14481
Wooseok Ha, Yuansi Chen, 19 Jul 2025, When few labeled target data suffice: a theory of semi-supervised domain adaptation via fine-tuning from multiple adaptive starts, https://arxiv.org/abs/2507.14661
Roy H. Jennings, Genady Paikin, Roy Shaul, Evgeny Soloveichik, 20 Jul 2025, Language Integration in Fine-Tuning Multimodal Large Language Models for Image-Based Regression, https://arxiv.org/abs/2507.14997
Hanyang Zhao, Haoxian Chen, Yucheng Guo, Genta Indra Winata, Tingting Ou, Ziyu Huang, David D. Yao, Wenpin Tang, 19 Jul 2025, Fine-Tuning Diffusion Generative Models via Rich Preference Optimization, https://arxiv.org/abs/2503.11720
Xingke Yang and Liang Li and Sicong Li and Liwei Guan and Hao Wang and Xiaoqi Qi and Jiang Liu and Xin Fu and Miao Pan, 9 Aug 2025, Fed MobiLLM: Efficient Federated LLM Fine-Tuning over Heterogeneous Mobile Devices via Server Assisted Side-Tuning, https://arxiv.org/abs/2508.06765
Brendan R. Hogan, Will Brown, Adel Boyarsky, Anderson Schneider, Yuriy Nevmyvaka, 9 Aug 2025, Technical Report: Full-Stack Fine-Tuning for the Q Programming Language, https://arxiv.org/abs/2508.06813
Amal Saadallah, Abdulaziz Al-Ademi, 11 Aug 2025, Adaptive Fine-Tuning via Pattern Specialization for Deep Time Series Forecasting, https://arxiv.org/abs/2508.07927
Bujar Raufi, 10 Aug 2025, Fine-Tuning Large Language Models Using EEG Microstate Features for Mental Workload Assessment, https://arxiv.org/abs/2508.07283
Zhaorui Tan, Tan Pan, Kaizhu Huang, Weimiao Yu, Kai Yao, Chen Jiang, Qiufeng Wang, Anh Nguyen, Xin Guo, Yuan Cheng, Xi Yang, 11 Aug 2025, Exploiting Layer Normalization Fine-tuning in Visual Transformer Foundation Models for Classification, https://arxiv.org/abs/2508.07577
Vishwas M. Shetty, Jiusi Zheng, Abeer Alwan, 11 Aug 2025, G-IFT: A Gated Linear Unit adapter with Iterative Fine-Tuning for Low-Resource Children's Speaker Verification, https://arxiv.org/abs/2508.07836
Xingke Yang and Liang Li and Zhiyi Wan and Sicong Li and Xiaoqi Qi and Jiang Liu and Tomoaki Ohtsuki and Xin Fu and Miao Pan, 9 Aug 2025, PAE MobiLLM: Privacy-Aware and Efficient LLM Fine-Tuning on the Mobile Device via Additive Side-Tuning, https://arxiv.org/abs/2507.01216
Mohammad Mehdi Rastikerdar, Jin Huang, Hui Guan, Deepak Ganesan, 11 Aug 2025, In-Situ Fine-Tuning of Wildlife Models in IoT-Enabled Camera Traps for Efficient Adaptation, https://arxiv.org/abs/2409.07796
Qingguo Wang, 10 Aug 2025, Accurate Measles Rash Detection via Vision Transformer Fine-Tuning, https://arxiv.org/abs/2005.09112
Atharva Nijasure, Tanya Chowdhury, James Allan, 10 Aug 2025, How Relevance Emerges: Interpreting LoRA Fine-Tuning in Reranking LLMs, https://arxiv.org/abs/2504.08780
Yining Huang,Bin Li,Keke Tang,Meilian Chen, 28 Jul 2025, LoRA-PAR: A Flexible Dual-System LoRA Partitioning Approach to Efficient LLM Fine-Tuning, https://arxiv.org/abs/2507.20999
Roman Mach\'a\v{c}ek and Anastasiia Grishina and Max Hort and Leon Moonen, 26 Jul 2025, The Impact of Fine-tuning Large Language Models on Automated Program Repair, https://arxiv.org/abs/2507.19909
Fabrizio Nunnari, Alakshendra Jyotsnaditya Ramkrishna Singh, Patrick Gebhard, 27 Jul 2025, Color histogram equalization and fine-tuning to improve expression recognition of (partially occluded) faces on sign language datasets, https://arxiv.org/abs/2507.20197
Wei Lu, Daniel L. Chen, Christian B. Hansen, 28 Jul 2025, Aligning Large Language Model Agents with Rational and Moral Preferences: A Supervised Fine-Tuning Approach, https://arxiv.org/abs/2507.20796
Punya Syon Pandey, Samuel Simko, Kellin Pelrine, Zhijing Jin, 28 Jul 2025, Accidental Vulnerability: Factors in Fine-Tuning that Shift Model Safeguards, https://arxiv.org/abs/2505.16789
Yifu Han and Geo Zhang, 27 Jul 2025, Reinforcement learning fine-tuning of language model for instruction following and math reasoning, https://arxiv.org/abs/2506.21560
Zixuan Chen and Weikai Lu and Xin Lin and Ziqian Zeng, 27 Jul 2025, SDD: Self-Degraded Defense against Malicious Fine-tuning, https://arxiv.org/abs/2507.21182
Zengyang Li, Yimeng Li, Binbin Huang, Peng Liang, Ran Mo, Hui Liu, Yutao Ma, 29 Jul 2025, Fine-Tuning Code Language Models to Detect Cross-Language Bugs, https://arxiv.org/abs/2507.21954
Aly M. Kassem, Zhuan Shi, Negar Rostamzadeh, Golnoosh Farnadi, 19 Jun 2025, Reviving Your MNEME: Predicting The Side Effects of LLM Unlearning and Fine-Tuning via Sparse Model Diffing, https://arxiv.org/abs/2507.21084
Georg Slamanig, Francesco Corti, Olga Saukh, 31 Jul 2025, From LLMs to Edge: Parameter-Efficient Fine-Tuning on Edge Devices, https://arxiv.org/abs/2507.23536
Sirine Arfa, Bernhard Vogginger, Christian Mayr, 31 Jul 2025, Hardware-Aware Fine-Tuning of Spiking Q-Networks on the SpiNNaker2 Neuromorphic Platform, https://arxiv.org/abs/2507.23562
Yan Zhu, Jingyang Zhu, Ting Wang, Yuanming Shi, Chunxiao Jiang and Khaled Ben Letaief, 31 Jul 2025, Satellite Federated Fine-Tuning for Foundation Models in Space Computing Power Networks, https://arxiv.org/abs/2504.10403
Wei Guo, Siyuan Lu, Yiqi Tong, Zhaojun Hu, Fuzhen Zhuang, Xiao Zhang, Tao Fan, Jin Dong, 31 Jul 2025, H2Tune: Federated Foundation Model Fine-Tuning with Hybrid Heterogeneity, https://arxiv.org/abs/2507.22633
Vishwesh Ramanathan, Tony Xu, Pushpak Pati, Faruk Ahmed, Maged Goubran, Anne L. Martel, 30 Jul 2025, ModalTune: Fine-Tuning Slide-Level Foundation Models with Multi-Modal Information for Multi-task Learning in Digital Pathology, https://arxiv.org/abs/2503.17564
Afshin Khadangi, Amir Sartipi, Igor Tchappi, Ramin Bahmani, Gilbert Fridgen, 30 Jul 2025, Efficient Differentially Private Fine-Tuning of LLMs via Reinforcement Learning, https://arxiv.org/abs/2507.22565
Yebo Wu, Jingguang Li, Zhijiang Guo and Li Li, 31 Jul 2025, Learning Like Humans: Resource-Efficient Federated Fine-Tuning through Cognitive Developmental Stages, https://arxiv.org/abs/2508.00041
Paul Albert, Frederic Z. Zhang, Hemanth Saratchandran, Anton van den Hengel, Ehsan Abbasnejad, 1 Aug 2025, Towards Higher Effective Rank in Parameter-efficient Fine-tuning using Khatri--Rao Product, https://arxiv.org/abs/2508.00230
Shayan Jalilian, Abdul Bais, 31 Jul 2025, SAM-PTx: Text-Guided Fine-Tuning of SAM with Parameter-Efficient, Parallel-Text Adapters, https://arxiv.org/abs/2508.00213
Prerana Ramkumar, 1 Aug 2025, SU-ESRGAN: Semantic and Uncertainty-Aware ESRGAN for Super-Resolution of Satellite and Drone Imagery with Fine-Tuning for Cross Domain Evaluation, https://arxiv.org/abs/2508.00750
Julian Lemmel, Manuel Kranzl, Adam Lamine, Philipp Neubauer, Radu Grosu, Sophie Neubauer, 1 Aug 2025, Online Fine-Tuning of Carbon Emission Predictions using Real-Time Recurrent Learning for State Space Models, https://arxiv.org/abs/2508.00804
Derin Cayir, Renjie Tao, Rashi Rungta, Kai Sun, Sean Chen, Haidar Khan, Minseok Kim, Julia Reinspach, Yue Liu, 3 Aug 2025, Refine-n-Judge: Curating High-Quality Preference Chains for LLM-Fine-Tuning, https://arxiv.org/abs/2508.01543
Yixin Shen, 4 Aug 2025, Kronecker-LoRA: hybrid Kronecker-LoRA adapters for scalable, sustainable fine-tuning, https://arxiv.org/abs/2508.01961
Amitava Das, Abhilekh Borah, Vinija Jain, Aman Chadha, 4 Aug 2025, AlignGuard-LoRA: Alignment-Preserving Fine-Tuning via Fisher-Guided Decomposition and Riemannian-Geodesic Collision Regularization, https://arxiv.org/abs/2508.02079
Yilun Liu, Yunpu Ma, Yuetian Lu, Shuo Chen, Zifeng Ding, Volker Tresp, 4 Aug 2025, Parameter-Efficient Routed Fine-Tuning: Mixture-of-Experts Demands Mixture of Adaptation Modules, https://arxiv.org/abs/2508.02587
Dongchi Huang, Zhirui Fang, Tianle Zhang, Yihang Li, Lin Zhao, Chunhe Xia, 4 Aug 2025, CO-RFT: Efficient Fine-Tuning of Vision-Language-Action Models through Chunked Offline Reinforcement Learning, https://arxiv.org/abs/2508.02219
Ayan Sengupta, Vaibhav Seth, Arinjay Pathak, Aastha Verma, Natraj Raman, Sriram Gopalakrishnan, Niladri Chatterjee, Tanmoy Chakraborty, 3 Aug 2025, Robust and Efficient Fine-tuning of LLMs with Bayesian Reparameterization of Low-Rank Adaptation, https://arxiv.org/abs/2411.04358
Yinbin Han, Meisam Razaviyayn, Renyuan Xu, 3 Aug 2025, Stochastic Control for Fine-tuning Diffusion Models: Optimality, Regularity, and Convergence, https://arxiv.org/abs/2412.18164
Jack Chen, Fazhong Liu, Naruto Liu, Yuhan Luo, Erqu Qin, Harry Zheng, Tian Dong, Haojin Zhu, Yan Meng, Xiao Wang, 4 Aug 2025, Step-wise Adaptive Integration of Supervised Fine-tuning and Reinforcement Learning for Task-Specific LLMs, https://arxiv.org/abs/2505.13026
Yidong Chai (1 and 2), Yang Liu (1 and 2), Yonghang Zhou (1 and 2), Jiaheng Xie (3), Daniel Dajun Zeng (4) ((1) School of Management, Hefei University of Technology, Hefei, China, (2) Key Laboratory of Process Optimization and Intelligent Decision-making, Ministry of Education, Hefei, China, (3) Department of Accounting and MIS, Lerner College of Business and Economics, University of Delaware, Newark, Delaware, U.S., (4) Institute of Automation, Chinese Academy of Sciences, Beijing, China), 31 Jul 2025, A Bayesian Hybrid Parameter-Efficient Fine-Tuning Method for Large Language Models, https://arxiv.org/abs/2508.02711
Jingyi Chen, Ju Seung Byun, Micha Elsner, Pichao Wang, Andrew Perrault, 5 Aug 2025, Fine-Tuning Text-to-Speech Diffusion Models Using Reinforcement Learning with Human Feedback, https://arxiv.org/abs/2508.03123
Yutong Chen, Jiandong Gao, Ji Wu, 5 Aug 2025, Towards Revealing the Effectiveness of Small-Scale Fine-tuning in R1-style Reinforcement Learning, https://arxiv.org/abs/2505.17988
Joel Walsh, Siddarth Mamidanna, Benjamin Nye, Mark Core, and Daniel Auerbach, 6 Aug 2025, Fine-tuning for Better Few Shot Prompting: An Empirical Comparison for Short Answer Grading, https://arxiv.org/abs/2508.04063
Ali Taheri Ghahrizjani, Alireza Taban, Qizhou Wang, Shanshan Ye, Abdolreza Mirzaei, Tongliang Liu, Bo Han, 6 Aug 2025, Forgetting: A New Mechanism Towards Better Large Language Model Fine-tuning, https://arxiv.org/abs/2508.04329
Liujian Tang, Shaokang Dong, Yijia Huang, Minqi Xiang, Hongtao Ruan, Bin Wang, Shuo Li, Zhihui Cao, Hailiang Pang, Heng Kong, He Yang, Mingxu Chai, Zhilin Gao, Xingyu Liu, Yingnan Fu, Jiaming Liu, Tao Gui, Xuanjing Huang, Yu-Gang Jiang, Qi Zhang, Kang Wang, Yunke Zhang, Yuran Wang, 19 Jul 2025, MagicGUI: A Foundational Mobile GUI Agent with Scalable Data Pipeline and Reinforcement Fine-tuning, https://arxiv.org/abs/2508.03700
Yanjie Dong, Haijun Zhang, Chengming Li, Song Guo, Victor C. M. Leung, Xiping Hu, 6 Aug 2025, Fine-Tuning and Deploying Large Language Models Over Edges: Issues and Approaches, https://arxiv.org/abs/2408.10691
Bohao Wu, Qingyun Wang, Yue Guo, 6 Aug 2025, Explain Less, Understand More: Jargon Detection via Personalized Parameter-Efficient Fine-tuning, https://arxiv.org/abs/2505.16227
Mahdi Nazari Ashani, Ali Asghar Alesheikh, Saba Kazemi, Kimya Kheirkhah, Yasin Mohammadi, Fatemeh Rezaie, Amir Mahdi Manafi, Hedieh Zarkesh, 6 Aug 2025, Fine-Tuning Small Language Models (SLMs) for Autonomous Web-based Geographical Information Systems (AWebGIS), https://arxiv.org/abs/2508.04846
Chang Tian, Matthew B. Blaschko, Mingzhe Xing, Xiuxing Li, Yinliang Yue, Marie-Francine Moens, 6 Aug 2025, Large Language Models Reasoning Abilities Under Non-Ideal Conditions After RL-Fine-Tuning, https://arxiv.org/abs/2508.04848
Nan Li, Wanting Yang, Marie Siew, Zehui Xiong, Binbin Chen, Shiwen Mao, Kwok-Yan Lam, 6 Aug 2025, Edge-Assisted Collaborative Fine-Tuning for Multi-User Personalized Artificial Intelligence Generated Content (AIGC), https://arxiv.org/abs/2508.04745
Dai Do, Manh Nguyen, Svetha Venkatesh, Hung Le, 7 Aug 2025, SPaRFT: Self-Paced Reinforcement Fine-Tuning for Large Language Models, https://arxiv.org/abs/2508.05015
Zhongheng Yang, Aijia Sun, Yushang Zhao, Yinuo Yang, Dannier Li, Chengrui Zhou, 7 Aug 2025, RLHF Fine-Tuning of LLMs for Alignment with Implicit User Feedback in Conversational Recommenders, https://arxiv.org/abs/2508.05289
Younwoo Choi, Muhammad Adil Asif, Ziwen Han, John Willes, Rahul G. Krishnan, 7 Aug 2025, Teaching LLMs How to Learn with Contextual Fine-Tuning, https://arxiv.org/abs/2503.09032
Jin Khye Tan (Faculty of Computer Science and Information Technology, Universiti Malaya), En Jun Choong, Ethan Jeremiah Chitty, Yan Pheng Choo, John Hsin Yang Wong, Chern Eu Cheah, 4 Aug 2025, Fine-Tuning Vision-Language Models for Markdown Conversion of Financial Tables in Malaysian Audited Financial Reports, https://arxiv.org/abs/2508.05669
Kaichuan Kong, Dongjie Liu, Xiaobo Jin, Guanggang Geng, Zhiying Li, Jian Weng, 6 Aug 2025, DMFI: Dual-Modality Fine-Tuning and Inference Framework for LLM-Based Insider Threat Detection, https://arxiv.org/abs/2508.05694
Han Gao, Timo Hartmann, Botao Zhong, Kai Lia, Hanbin Luo, 5 Aug 2025, Domain-Specific Fine-Tuning and Prompt-Based Learning: A Comparative Study for developing Natural Language-Based BIM Information Retrieval Systems, https://arxiv.org/abs/2508.05676
Jucheng Hu, Surong Yang, Lijun Wu, Dongzhan Zhou, 8 Aug 2025, DONOD: Efficient and Generalizable Instruction Fine-Tuning for LLMs via Model-Intrinsic Dataset Pruning, https://arxiv.org/abs/2504.14810
Mahmoud Salhab, Shameed Sait, Mohammad Abusheikh, Hasan Abusheikh, 12 Aug 2025, Munsit at NADI 2025 Shared Task 2: Pushing the Boundaries of Multidialectal Arabic ASR with Weakly Supervised Pretraining and Continual Supervised Fine-tuning, https://arxiv.org/abs/2508.08912
Dong Wang, Haris \v{S}iki\'c, Lothar Thiele, Olga Saukh, 12 Aug 2025, Forget the Data and Fine-Tuning! Just Fold the Network to Compress, https://arxiv.org/abs/2502.10216
Sajjad Ghiasvand and Haniyeh Ehsani Oskouie and Mahnoosh Alizadeh and Ramtin Pedarsani, 12 Aug 2025, Few-Shot Adversarial Low-Rank Fine-Tuning of Vision-Language Models, https://arxiv.org/abs/2505.15130
Liang Chen, Xueting Han, Li Shen, Jing Bai, Kam-Fai Wong, 12 Aug 2025, Vulnerability-Aware Alignment: Mitigating Uneven Forgetting in Harmful Fine-Tuning, https://arxiv.org/abs/2506.03850
Jan Tauberschmidt, Sophie Fellenz, Sebastian J. Vollmer, Andrew B. Duncan, 5 Aug 2025, Physics-Constrained Fine-Tuning of Flow-Matching Models for Generation and Inverse Problems, https://arxiv.org/abs/2508.09156
Bokeng Zheng, Jianqiang Zhong, Jiayi Liu, Xiaoxi Zhang, 13 Aug 2025, Decentralized Rank Scheduling for Energy-Constrained Multi-Task Federated Fine-Tuning in Edge-Assisted IoV Networks, https://arxiv.org/abs/2508.09532
Zainab Khan, Ahmed Hussain, Mukesh Thakur, Arto Hellas, and Panos Papadimitratos, 12 Aug 2025, NEFMind: Parameter-Efficient Fine-Tuning of Open-Source LLMs for Telecom APIs Automation, https://arxiv.org/abs/2508.09240
Basile Lewandowski, Robert Birke, Lydia Y. Chen, 14 Aug 2025, Match & Choose: Model Selection Framework for Fine-tuning Text-to-Image Diffusion Models, https://arxiv.org/abs/2508.10993
Wenhao Zhang, Yuexiang Xie, Yuchang Sun, Yanxi Chen, Guoyin Wang, Yaliang Li, Bolin Ding, Jingren Zhou, 15 Aug 2025, On-Policy RL Meets Off-Policy Experts: Harmonizing Supervised Fine-Tuning and Reinforcement Learning via Dynamic Weighting, https://arxiv.org/abs/2508.11408
Baihong Qian, Haotian Fan, Wenjie Liao, Yunqiu Wang, Tao Li, and Junhui Cui, 15 Aug 2025, Better Supervised Fine-tuning for VQA: Integer-Only Loss, https://arxiv.org/abs/2508.11170
Axel Delaval, Shujian Yang, Haicheng Wang, Han Qiu, Jialiang Lu, 15 Aug 2025, ToxiFrench: Benchmarking and Enhancing Language Models via CoT Fine-Tuning for French Toxicity Detection, https://arxiv.org/abs/2508.11281
Yuan Li, Zhengzhong Liu, and Eric Xing, 16 Aug 2025, Data Mixing Optimization for Supervised Fine-Tuning of Large Language Models, https://arxiv.org/abs/2508.11953
Daria Diatlova, Nikita Balagansky, Alexander Varlamov, Egor Spirin, 16 Aug 2025, VARAN: Variational Inference for Self-Supervised Speech Models Fine-Tuning on Downstream Tasks, https://arxiv.org/abs/2508.12061
Minseon Kim, Jin Myung Kwak, Lama Alssum, Bernard Ghanem, Philip Torr, David Krueger, Fazl Barez, Adel Bibi, 17 Aug 2025, Rethinking Safety in LLM Fine-tuning: An Optimization Perspective, https://arxiv.org/abs/2508.12531
Yuhao Zhou, Jindi Lv, Yuxin Tian, Dan Si, Qing Ye, Jiancheng Lv, 18 Aug 2025, Deploying Models to Non-participating Clients in Federated Learning without Fine-tuning: A Hypernetwork-based Approach, https://arxiv.org/abs/2508.12673
Manning Zhu, Songtao Guo, Pengzhan Zhou, Yansong Ning, Chang Han, Dewen Qiao, 18 Aug 2025, FedSODA: Federated Fine-tuning of LLMs via Similarity Group Pruning and Orchestrated Distillation Alignment, https://arxiv.org/abs/2508.12727
Julia Sammartino, Libby Barak, Jing Peng, Anna Feldman, 15 Aug 2025, When Does Language Transfer Help? Sequential Fine-Tuning for Cross-Lingual Euphemism Detection, https://arxiv.org/abs/2508.11831
Shiwei Li, Xiandi Luo, Xing Tang, Haozhao Wang, Hao Chen, Weihong Luo, Yuhua Li, Xiuqiang He, Ruixuan Li, 17 Aug 2025, Beyond Zero Initialization: Investigating the Impact of Non-Zero Initialization on LoRA Fine-Tuning Dynamics, https://arxiv.org/abs/2505.23194
Rafi Ibn Sultan, Chengyin Li, Hui Zhu, Prashant Khanduri, Marco Brocanelli, Dongxiao Zhu, 15 Aug 2025, GeoSAM: Fine-tuning SAM with Multi-Modal Prompts for Mobility Infrastructure Segmentation, https://arxiv.org/abs/2311.11319
Keyu Chen, Wenchao Sun, Hao Cheng, Sifa Zheng, 18 Aug 2025, RIFT: Closed-Loop RL Fine-Tuning for Realistic and Controllable Traffic Simulation, https://arxiv.org/abs/2505.03344
Dongyoon Hahm, Taywon Min, Woogyeol Jin, Kimin Lee, 19 Aug 2025, Unintended Misalignment from Agentic Fine-Tuning: Risks and Mitigation, https://arxiv.org/abs/2508.14031
Hassan Barmandah, 19 Aug 2025, Saudi-Dialect-ALLaM: LoRA Fine-Tuning for Dialectal Arabic Generation, https://arxiv.org/abs/2508.13525
Eric Nuertey Coleman, Luigi Quarantiello, Ziyue Liu, Qinwen Yang, Samrat Mukherjee, Julio Hurtado and Vincenzo Lomonaco, 19 Aug 2025, Parameter-Efficient Continual Fine-Tuning: A Survey, https://arxiv.org/abs/2504.13822
Yajie Zhou and Xiaoyi Pang and Zhibo Wang, 20 Aug 2025, AFLoRA: Adaptive Federated Fine-Tuning of Large Language Models with Resource-Aware Low-Rank Adaption, https://arxiv.org/abs/2505.24773
Xujia Wang, Yunjia Qi, Bin Xu, 20 Aug 2025, LoSiA: Efficient High-Rank Fine-Tuning via Subnet Localization and Optimization, https://arxiv.org/abs/2507.04487
Mayla R. Boguslav, Adam Kiehl, David Kott, G. Joseph Strecker, Tracy Webb, Nadia Saklou, Terri Ward, Michael Kirby, 20 Aug 2025, Fine-tuning foundational models to code diagnoses from veterinary health records, https://arxiv.org/abs/2410.15186
Huichi Zhou, Yihang Chen, Siyuan Guo, Xue Yan, Kin Hei Lee, Zihan Wang, Ka Yiu Lee, Guchun Zhang, Kun Shao, Linyi Yang, Jun Wang, 22 Aug 2025, AgentFly: Fine-tuning LLM Agents without Fine-tuning LLMs, https://arxiv.org/abs/2508.16153
Sungmin Kang, Jisoo Kim, Salman Avestimehr, Sunwoo Lee, 22 Aug 2025, GEM: A Scale-Aware and Distribution-Sensitive Sparse Fine-Tuning Framework for Effective Downstream Adaptation, https://arxiv.org/abs/2508.16191
Hangzhan Jin, Sicheng Lv, Sifan Wu, Mohammad Hamdaqa, 22 Aug 2025, RL Is Neither a Panacea Nor a Mirage: Understanding Supervised vs. Reinforcement Learning Fine-Tuning for LLMs, https://arxiv.org/abs/2508.16546
Wenqiao Zhu, Ji Liu, Rongjuncheng Zhang, Haipang Wu, Yulun Zhang, 21 Aug 2025, CARFT: Boosting LLM Reasoning via Contrastive Learning with Annotated Chain-of-Thought-based Reinforced Fine-Tuning, https://arxiv.org/abs/2508.15868
Sajjad Ghiasvand, Mahnoosh Alizadeh, Ramtin Pedarsani, 21 Aug 2025, Decentralized Low-Rank Fine-Tuning of Large Language Models, https://arxiv.org/abs/2501.15361
Hanyang Zhao, Haoxian Chen, Ji Zhang, David D. Yao and Wenpin Tang, 21 Aug 2025, Score as Action: Fine-Tuning Diffusion Generative Models by Continuous-time Reinforcement Learning, https://arxiv.org/abs/2502.01819
Jack Youstra, Mohammed Mahfoud, Yang Yan, Henry Sleight, Ethan Perez, Mrinank Sharma, 23 Aug 2025, Towards Safeguarding LLM Fine-tuning APIs against Cipher Attacks, https://arxiv.org/abs/2508.17158
Wenhong Zhu, Ruobing Xie, Rui Wang, Xingwu Sun, Di Wang, Pengfei Liu, 25 Aug 2025, Proximal Supervised Fine-Tuning, https://arxiv.org/abs/2508.17784
Bin Pan, Shiyu Shen, Zongbin Wang, Zhenwei Shi and Xia Xu, 23 Aug 2025, Preserving Domain Generalization in Fine-Tuning via Joint Parameter Selection, https://arxiv.org/abs/2508.16976
Haojie Zhang, 24 Aug 2025, DropLoRA: Sparse Low-Rank Adaptation for Parameter-Efficient Fine-Tuning, https://arxiv.org/abs/2508.17337