Fine Tuning

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
• Yuhao Zhang, Shaoming Duan, Jinhang Su, Chuanyi Liu, Peiyi Han, 4 Sep 2025, SPFT-SQL: Enhancing Large Language Model for Text-to-SQL Parsing by Self-Play Fine-Tuning, https://arxiv.org/abs/2509.03937
• Junyu Yan, Feng Chen, Yuyang Xue, Yuning Du, Konstantinos Vilouras, Sotirios A. Tsaftaris, Steven McDonagh, 4 Sep 2025, SWiFT: Soft-Mask Weight Fine-tuning for Bias Mitigation, https://arxiv.org/abs/2508.18826
• Wei Huang, Huang Wei, Yinggui Wang, 4 Sep 2025, DaMoC: Efficiently Selecting the Optimal Large Language Model for Fine-tuning Domain Tasks Based on Data and Model Compression, https://arxiv.org/abs/2509.01221
• Cheng Peng, Xinyu Dong, Mengxian Lyu, Daniel Paredes, Yaoyun Zhang, Yonghui Wu, 5 Sep 2025, A Study of Large Language Models for Patient Information Extraction: Model Architecture, Fine-Tuning Strategy, and Multi-task Instruction Tuning, https://arxiv.org/abs/2509.04753
• Tiansheng Huang, Gautam Bhattacharya, Pratik Joshi, Josh Kimball, Ling Liu, 5 Sep 2025, Antidote: Post-fine-tuning Safety Alignment for Large Language Models against Harmful Fine-tuning, https://arxiv.org/abs/2408.09600
• William F. Shen, Xinchi Qiu, Nicola Cancedda, Nicholas D. Lane, 5 Sep 2025, Don't Make It Up: Preserving Ignorance Awareness in LLM Fine-Tuning, https://arxiv.org/abs/2506.14387
• Gang Hu, Yinglei Teng, Pengfei Wu, and Nan Wang, 26 Aug 2025, FFT-MoE: Efficient Federated Fine-Tuning for Foundation Models via Large-scale Sparse MoE under Heterogeneous Edge, https://arxiv.org/abs/2508.18663
• Qing Xiao, Yingshan Peng and PeiPei Zhang, 26 Aug 2025, Cross-Learning Fine-Tuning Strategy for Dysarthric Speech Recognition Via CDSD database, https://arxiv.org/abs/2508.18732
• Qihang Ai, Pi Bu, Yue Cao, Yingyao Wang, Jihao Gu, Jingxuan Xing, Zekun Zhu, Wei Jiang, Zhicheng Zheng, Jun Song, Yuning Jiang, Bo Zheng, 27 Aug 2025, InquireMobile: Teaching VLM-based Mobile Agent to Request Human Assistance via Reinforcement Fine-Tuning, https://arxiv.org/abs/2508.19679
• Dikshant Sagar, Kaiwen Yu, Alejandro Yankelevich, Jianming Bian, Pierre Baldi, 26 Aug 2025, Fine-Tuning Vision-Language Models for Neutrino Event Analysis in High-Energy Physics Experiments, https://arxiv.org/abs/2508.19376
• Yuhang Liu, Tao Li, Zhehao Huang, Zuopeng Yang, and Xiaolin Huang, 27 Aug 2025, Bi-LoRA: Efficient Sharpness-Aware Minimization for Fine-Tuning Large-Scale Models, https://arxiv.org/abs/2508.19564
• Fahao Chen, Jie Wan, Peng Li, Zhou Su, Dongxiao Yu, 26 Aug 2025, Federated Fine-Tuning of Sparsely-Activated Large Language Models on Resource-Constrained Devices, https://arxiv.org/abs/2508.19078
• Manuel Mosquera, Melissa Robles, Johan Rodriguez, Ruben Manrique, 26 Aug 2025, Improving Low-Resource Translation with Dictionary-Guided Fine-Tuning and RL: A Spanish-to-Wayuunaiki Study, https://arxiv.org/abs/2508.19481
• Fatema Siddika, Md Anwar Hossen, J. Pablo Mu\~noz, Tanya Roosta, Anuj Sharma, Ali Jannesari, 27 Aug 2025, FedReFT: Federated Representation Fine-Tuning with All-But-Me Aggregation, https://arxiv.org/abs/2508.20295
• Weitao Feng, Lixu Wang, Tianyi Wei, Jie Zhang, Chongyang Gao, Sinong Zhan, Peizhuo Lv, Wei Dong, 28 Aug 2025, Token Buncher: Shielding LLMs from Harmful Reinforcement Learning Fine-Tuning, https://arxiv.org/abs/2508.20697
• Jinyuan Feng, Chaopeng Wei, Tenghai Qiu, Tianyi Hu, Zhiqiang Pu, 28 Aug 2025, CoMoE: Contrastive Representation for Mixture-of-Experts in Parameter-Efficient Fine-tuning, https://arxiv.org/abs/2505.17553
• Ali Nazari and Michael Weiss, 28 Aug 2025, Fine-Tuning Topics through Weighting Aspect Keywords, https://arxiv.org/abs/2502.08496
• Jessica Liang, Anirudh Bharadwaj, 29 Aug 2025, QR-LoRA: QR-Based Low-Rank Adaptation for Efficient Fine-Tuning of Large Language Models, https://arxiv.org/abs/2508.21810
• Guofu Liao, Taotao Wang, Shengli Zhang, Jiqun Zhang, Shi Long, and Dacheng Tao, 29 Aug 2025, zkLoRA: Fine-Tuning Large Language Models with Verifiable Security via Zero-Knowledge Proofs, https://arxiv.org/abs/2508.21393
• Zinan Tang, Xin Gao, Qizhi Pei, Zhuoshi Pan, Mengzhang Cai, Jiang Wu, Conghui He and Lijun Wu, 29 Aug 2025, Middo: Model-Informed Dynamic Data Optimization for Enhanced LLM Fine-Tuning via Closed-Loop Learning, https://arxiv.org/abs/2508.21589
• Jo\~ao Valente, Atabak Dehban, Rodrigo Ventura, 29 Aug 2025, CAD2DMD-SET: Synthetic Generation Tool of Digital Measurement Device CAD Model Datasets for fine-tuning Large Vision-Language Models, https://arxiv.org/abs/2508.21732
• Yanxiao Zhao, Yaqian Li, Zihao Bo, Rinyoichi Takezoe, Haojia Hui, Mo Guang, Lei Ren, Xiaolin Qin, Kaiwen Long, 31 Aug 2025, SATQuest: A Verifier for Logical Reasoning Evaluation and Reinforcement Fine-Tuning of LLMs, https://arxiv.org/abs/2509.00930
• Elie Thellier (EPIONE), Huiyu Li (EPIONE), Nicholas Ayache (EPIONE), Herv\'e Delingette (EPIONE), 20 Aug 2025, Mitigating Data Exfiltration Attacks through Layer-Wise Learning Rate Decay Fine-Tuning, https://arxiv.org/abs/2509.00027
• Shikun Liu, Deyu Zou, Nima Shoghi, Victor Fung, Kai Liu, Pan Li, 30 Aug 2025, RoFt-Mol: Benchmarking Robust Fine-Tuning with Molecular Graph Foundation Models, https://arxiv.org/abs/2509.00614
• Xinlu Zhang, Na Yan, Yang Su, Yansha Deng, Toktam Mahmoodi, 1 Sep 2025, Communication-Aware Knowledge Distillation for Federated LLM Fine-Tuning over Wireless Networks, https://arxiv.org/abs/2509.01750
• Wenlong Mou, 2 Sep 2025, Is RL fine-tuning harder than regression? A PDE learning approach for diffusion models, https://arxiv.org/abs/2509.02528
• Asif Mohammed Saad, Umme Niraj Mahi, 2 Sep 2025, SegFormer Fine-Tuning with Dropout: Advancing Hair Artifact Removal in Skin Lesion Analysis, https://arxiv.org/abs/2509.02156
• Sifeng Shang, Jiayi Zhou, Chenyu Lin, Minxian Li, Kaiyang Zhou, 1 Sep 2025, Fine-tuning Quantized Neural Networks with Zeroth-order Optimization, https://arxiv.org/abs/2505.13430
• Xingyu Su, Xiner Li, Masatoshi Uehara, Sunwoo Kim, Yulai Zhao, Gabriele Scalia, Ehsan Hajiramezanali, Tommaso Biancalani, Degui Zhi, Shuiwang Ji, 30 Aug 2025, Iterative Distillation for Reward-Guided Fine-Tuning of Diffusion Models in Biomolecular Design, https://arxiv.org/abs/2507.00445
• Xin Chen, Shuaijun Chen, Omid Tavallaie, Nguyen Tran, Shuhuang Xiang, Albert Zomaya, 30 Aug 2025, Convergence Analysis of Aggregation-Broadcast in LoRA-enabled Distributed Fine-Tuning, https://arxiv.org/abs/2508.01348
• Jianwei Wang, Chengming Shi, Junyao Yang, Haoran Li, Qianli Ma, Huiping Zhuang, Cen Chen and Ziqian Zeng, 31 Aug 2025, RewardDS: Privacy-Preserving Fine-Tuning for Large Language Models via Reward Driven Data Synthesis, https://arxiv.org/abs/2502.18517
• Linus Jern, Valter Uotila, Cong Yu, Bo Zhao, 1 Sep 2025, Agent-Q: Fine-Tuning Large Language Models for Quantum Circuit Generation and Optimization, https://arxiv.org/abs/2504.11109
• Christopher Subia-Waud (Rayonlabs Team), 3 Sep 2025, Gradients: When Markets Meet Fine-tuning -- A Distributed Approach to Model Optimisation, https://arxiv.org/abs/2506.07940
• Xiang Yuan, Jun Shu, Deyu meng, Zongben Xu, 31 Aug 2025, Feed Two Birds with One Scone: Exploiting Function-Space Regularization for Both OOD Robustness and ID Fine-Tuning Performance, https://arxiv.org/abs/2509.05328
• ZiXuan Zhang, Bowen Hao, Yingjie Li, Hongzhi Yin, 6 Sep 2025, ZhiFangDanTai: Fine-tuning Graph-based Retrieval-Augmented Generation Model for Traditional Chinese Medicine Formula, https://arxiv.org/abs/2509.05867
• Joe Wilder, Nikhil Kadapala, Benji Xu, Mohammed Alsaadi, Aiden Parsons, Mitchell Rogers, Palash Agarwal, Adam Hassick, Laura Dietz, 8 Sep 2025, UNH at CheckThat! 2025: Fine-tuning Vs Prompting in Claim Extraction, https://arxiv.org/abs/2509.06883
• Lishan Yang, Nam Kha Nguygen, Po Hu, Wei Emma Zhang, Yanjun Shu, Mong Yuan Sim and Weitong Chen, 1 Sep 2025, FediLoRA: Heterogeneous LoRA for Federated Multimodal Fine-tuning under Missing Modalities, https://arxiv.org/abs/2509.06984
• Xiao Li and Bharat Gandhi and Ming Zhan and Mohit Nehra and Zhicheng Zhang and Yuchen Sun and Meijia Song and Naisheng Zhang and Xi Wang, 9 Sep 2025, Fine-Tuning Vision-Language Models for Visual Navigation Assistance, https://arxiv.org/abs/2509.07488
• Michele Joshua Maggini, Dhia Merzougui, Rabiraj Bandyopadhyay, Ga\"el Dias, Fabrice Maurel, Pablo Gamallo, 9 Sep 2025, Are LLMs Enough for Hyperpartisan, Fake, Polarized and Harmful Content Detection? Evaluating In-Context Learning vs. Fine-Tuning, https://arxiv.org/abs/2509.07768
• Jiahao Chen, Zhiyuan Huang, Yurou Liu, Bing Su, 12 Sep 2025, LoFT: Parameter-Efficient Fine-Tuning for Long-tailed Semi-Supervised Learning in Open-World Scenarios, https://arxiv.org/abs/2509.09926
• Himanshu Thakur, Eshani Agrawal, Smruthi Mukund, 18 Aug 2025, Personas within Parameters: Fine-Tuning Small Language Models with Low-Rank Adapters to Mimic User Behaviors, https://arxiv.org/abs/2509.09689
• Talha Tahir, 8 Sep 2025, The Thinking Therapist: Training Large Language Models to Deliver Acceptance and Commitment Therapy using Supervised Fine-Tuning and Odds Ratio Policy Optimization, https://arxiv.org/abs/2509.09712
• Hao Zhang, Bo Huang, Zhenjia Li, Xi Xiao, Hui Yi Leong, Zumeng Zhang, Xinwei Long, Tianyang Wang, Hao Xu, 11 Sep 2025, Sensitivity-LoRA: Low-Load Sensitivity-Based Fine-Tuning for Large Language Models, https://arxiv.org/abs/2509.09119
• Honghui Xu, Shiva Shrestha, Wei Chen, Zhiyuan Li, Zhipeng Cai, 11 Sep 2025, DP-FedLoRA: Privacy-Enhanced Federated Fine-Tuning for On-Device Large Language Models, https://arxiv.org/abs/2509.09097
• Leonardo Matone, Ben Abramowitz, Ben Armstrong, Avinash Balakrishnan, Nicholas Mattei, 11 Sep 2025, DeepVoting: Learning and Fine-Tuning Voting Rules with Canonical Embeddings, https://arxiv.org/abs/2408.13630
• Marko Tuononen, Heikki Penttinen, Ville Hautam\"aki, 19 Sep 2025, Targeted Fine-Tuning of DNN-Based Receivers via Influence Functions, https://arxiv.org/abs/2509.15950
• Baichuan Huang, Ananth Balashankar, Amir Aminifar, 19 Sep 2025, BEFT: Bias-Efficient Fine-Tuning of Language Models, https://arxiv.org/abs/2509.15974
• Youngwon Choi, Jaeyoon Jung, Hyeonyu Kim, Huu-Kim Nguyen, Hwayeon Kim, 18 Sep 2025, Exploring Fine-Tuning of Large Audio Language Models for Spoken Language Understanding under Limited Speech data, https://arxiv.org/abs/2509.15389
• Ishika Agarwal, Dilek Hakkani-T\"ur, 19 Sep 2025, Neural Networks for Learnable and Scalable Influence Estimation of Instruction Fine-Tuning Data, https://arxiv.org/abs/2502.09969
• Shiwan Zhao, Xuyang Zhao, Jiaming Zhou, Aobo Kong, Qicheng Li, Yong Qin, 19 Sep 2025, Mind the Gap: Data Rewriting for Stable Off-Policy Supervised Fine-Tuning, https://arxiv.org/abs/2509.15157
• MSR Avinash, 7 Sep 2025, Profiling LoRA/QLoRA Fine-Tuning Efficiency on Consumer GPUs: An RTX 4060 Case Study, https://arxiv.org/abs/2509.12229
• Hangzhan Jin, Sitao Luan, Sicheng Lyu, Guillaume Rabusseau, Reihaneh Rabbany, Doina Precup, Mohammad Hamdaqa, 8 Sep 2025, RL Fine-Tuning Heals OOD Forgetting in SFT, https://arxiv.org/abs/2509.12235
• Mengyi Deng, Xin Li, Tingyu Zhu, Zhicheng Yang, Zhijiang Guo, Wei Wang, 16 Sep 2025, When Inverse Data Outperforms: Exploring the Pitfalls of Mixed Data in Multi-Stage Fine-Tuning, https://arxiv.org/abs/2509.13079
• Bo Yin, Xingyi Yang, Xinchao Wang, 16 Sep 2025, Don't Forget the Nonlinearity: Unlocking Activation Functions in Efficient Fine-Tuning, https://arxiv.org/abs/2509.13240
• Rodrigo M Carrillo-Larco, 16 Sep 2025, LLMs for energy and macronutrients estimation using only text data from 24-hour dietary recalls: a parameter-efficient fine-tuning experiment using a 10-shot prompt, https://arxiv.org/abs/2509.13268
• Kiho Lee, Jungkon Kim, Doowon Kim, Hyoungshick Kim, 16 Sep 2025, A Systematic Evaluation of Parameter-Efficient Fine-Tuning Methods for the Security of Code LLMs, https://arxiv.org/abs/2509.12649
• Yao Liang, Dongcheng Zhao, Feifei Zhao, Guobin Shen, Yuwei Wang, Dongqi Liang, Yi Zeng, 16 Sep 2025, MVPBench: A Benchmark and Fine-Tuning Framework for Aligning Large Language Models with Diverse Human Values, https://arxiv.org/abs/2509.08022
• Pengcheng Luo, Yunyang Zhao, Bowen Zhang, Genke Yang, Boon-Hee Soong, Chau Yuen, 30 Aug 2025, SABR: A Stable Adaptive Bitrate Framework Using Behavior Cloning Pretraining and Reinforcement Learning Fine-Tuning, https://arxiv.org/abs/2509.10486
• Milan Marocchi, Matthew Fynn, Kayapanda Mandana, Yue Rong, 15 Sep 2025, Scaling to Multimodal and Multichannel Heart Sound Classification: Fine-Tuning Wav2Vec 2.0 with Synthetic and Augmented Biosignals, https://arxiv.org/abs/2509.11606
• Lei Wang, Jieming Bian, Letian Zhang, Jie Xu, 18 Sep 2025, Adaptive LoRA Experts Allocation and Selection for Federated Fine-Tuning, https://arxiv.org/abs/2509.15087
• Yeongbin Seo and Dongha Lee and Jaehyung Kim and Jinyoung Yeo, 18 Sep 2025, Fast and Fluent Diffusion Language Models via Convolutional Decoding and Rejective Fine-tuning, https://arxiv.org/abs/2509.15188
• Gustavo Sandoval, Denys Fenchenko and Junyao Chen, 15 Sep 2025, Early Approaches to Adversarial Fine-Tuning for Prompt Injection Defense: A 2022 Study of GPT-3 and Contemporary Models, https://arxiv.org/abs/2509.14271
• Chenjun Li, Laurin Lux, Alexander H. Berger, Martin J. Menten, Mert R. Sabuncu, Johannes C. Paetzold, 17 Sep 2025, Fine-tuning Vision Language Models with Graph-based Knowledge for Explainable Medical Image Analysis, https://arxiv.org/abs/2503.09808
• Yu Cheng Chih, Yong Hao Hou, 10 Sep 2025, Low-Resource Fine-Tuning for Multi-Task Structured Information Extraction with a Billion-Parameter Instruction-Tuned Model, https://arxiv.org/abs/2509.08381
• Alejandro Moreno Arcas, Albert Sanchis, Jorge Civera, Alfons Juan, 10 Sep 2025, HOFT: Householder Orthogonal Fine-tuning, https://arxiv.org/abs/2505.16531
• Pittawat Taveekitworachai, Potsawee Manakul, Sarana Nutanong, Kunat Pipatanakul, 10 Sep 2025, Prior Prompt Engineering for Reinforcement Fine-Tuning, https://arxiv.org/abs/2505.14157
• Shambhavi Krishna, Atharva Naik, Chaitali Agarwal, Sudharshan Govindan, Taesung Lee, Haw-Shiuan Chang, 17 Sep 2025, Latent Traits and Cross-Task Transfer: Deconstructing Dataset Interactions in LLM Fine-tuning, https://arxiv.org/abs/2509.13624
• Haoteng Yin, Rongzhe Wei, Eli Chien, Pan Li, 16 Sep 2025, Privately Learning from Graphs with Applications in Fine-tuning Large Language Models, https://arxiv.org/abs/2410.08299
• Adel ElZemity, Budi Arief and Shujun Li, 17 Sep 2025, CyberLLMInstruct: A Pseudo-malicious Dataset Revealing Safety-performance Trade-offs in Cyber Security LLM Fine-tuning, https://arxiv.org/abs/2503.09334
• Humaid Ibrahim, Nikolai Rozanov, Marek Rei, 1 Oct 2025, Fine-tuning with RAG for Improving LLM Learning of New Skills, https://arxiv.org/abs/2510.01375
• Jaeyeon Kim, Seunggeun Kim, Taekyun Lee, David Z. Pan, Hyeji Kim, Sham Kakade, Sitan Chen, 1 Oct 2025, Fine-Tuning Masked Diffusion for Provable Self-Correction, https://arxiv.org/abs/2510.01384
• Haotian Xiang, Jinwen Xu, Qin Lu, 1 Oct 2025, Fine-tuning LLMs with variational Bayesian last layer for high-dimensional Bayesian optimization, https://arxiv.org/abs/2510.01471
• Zhaoyi Li, Jingtao Ding, Yong Li, Shihua Li, 2 Oct 2025, Fine-Tuning Flow Matching via Maximum Likelihood Estimation of Reconstructions, https://arxiv.org/abs/2510.02081
• Neal Gregory Lawton, Alfy Samuel, Anoop Kumar, Daben Liu, 2 Oct 2025, A Comparison of Independent and Joint Fine-tuning Strategies for Retrieval-Augmented Generation, https://arxiv.org/abs/2510.01600
• Nilay Naharas, Dang Nguyen, Nesihan Bulut, Mohammadhossein Bateni, Vahab Mirrokni, Baharan Mirzasoleiman, 1 Oct 2025, Data Selection for Fine-tuning Vision Language Models via Cross Modal Alignment Trajectories, https://arxiv.org/abs/2510.01454
• Kathy Garcia and Leyla Isik, 1 Oct 2025, Aligning Video Models with Human Social Judgments via Behavior-Guided Fine-Tuning, https://arxiv.org/abs/2510.01502
• Junseo Hwang, Wonguk Cho, Taesup Kim, 2 Oct 2025, PiCa: Parameter-Efficient Fine-Tuning with Column Space Projection, https://arxiv.org/abs/2505.20211
• Kaustubh Ponkshe, Raghav Singhal, Eduard Gorbunov, Alexey Tumanov, Samuel Horvath, Praneeth Vepakomma, 2 Oct 2025, Initialization using Update Approximation is a Silver Bullet for Extremely Efficient Low-Rank Fine-Tuning, https://arxiv.org/abs/2411.19557
• Raghav Singhal, Kaustubh Ponkshe, Rohit Vartak, Praneeth Vepakomma, 2 Oct 2025, ABBA-Adapters: Efficient and Expressive Fine-Tuning of Foundation Models, https://arxiv.org/abs/2505.14238
• Tian Xia, Matthew Sinclair, Andreas Schuh, Fabio De Sousa Ribeiro, Raghav Mehta, Rajat Rasal, Esther Puyol-Ant\'on, Samuel Gerber, Kersten Petersen, Michiel Schaap, Ben Glocker, 2 Oct 2025, Segmentor-Guided Counterfactual Fine-Tuning for Locally Coherent and Targeted Image Synthesis, https://arxiv.org/abs/2509.24913
• Abdulhady Abas Abdullah, Arkaitz Zubiaga, Seyedali Mirjalili, Amir H. Gandomi, Fatemeh Daneshfar, Mohammadsadra Amini, Alan Salam Mohammed, Hadi Veisi, 14 Oct 2025, Evolution of meta's llama models and parameter-efficient fine-tuning of large language models: a survey, https://arxiv.org/abs/2510.12178
• Yukun Zhang, and Qi Dong, 14 Oct 2025, Hierarchical Alignment: Surgical Fine-Tuning via Functional Layer Specialization in Large Language Models, https://arxiv.org/abs/2510.12044
• Sijing Xie, Dingzhu Wen, Changsheng You, Qimei Chen, Mehdi Bennis, and Kaibin Huang, 14 Oct 2025, FedLoDrop: Federated LoRA with Dropout for Generalized LLM Fine-tuning, https://arxiv.org/abs/2510.12078
• Rohan Kadekodi, Zhan Jin, Keisuke Kamahori, Yile Gu, Sean Khatiri, Noah H. Bayindirli, Sergey Gorbunov and Baris Kasikci, 30 Sep 2025, DualTune: Decoupled Fine-Tuning for On-Device Agentic Systems, https://arxiv.org/abs/2510.00229
• Xin Yu, Cong Xie, Ziyu Zhao, Tiantian Fan, Lingzhou Xue, Zhi Zhang, 30 Sep 2025, PrunedLoRA: Robust Gradient-Based structured pruning for Low-rank Adaptation in Fine-tuning, https://arxiv.org/abs/2510.00192
• Zhanda Zhu, Qidong Su, Yaoyao Ding, Kevin Song, Shang Wang, and Gennady Pekhimenko, 30 Sep 2025, LoRAFusion: Efficient LoRA Fine-Tuning for LLMs, https://arxiv.org/abs/2510.00206
• Ayush Jain and Andrea Montanari and Eren Sasoglu, 1 Oct 2025, Train on Validation (ToV): Fast data selection with applications to fine-tuning, https://arxiv.org/abs/2510.00386
• Kairun Zhang, Haoyu Li, Yanjun Zhao, Yifan Sun, Huan Zhang, 1 Oct 2025, Learning a Zeroth-Order Optimizer for Fine-Tuning LLMs, https://arxiv.org/abs/2510.00419
• Ali Dadsetan, Frank Rudzicz, 1 Oct 2025, Sample-Efficient Differentially Private Fine-Tuning via Gradient Matrix Denoising, https://arxiv.org/abs/2510.01137
• Zhexiong Liu, Diane Litman, 30 Sep 2025, Efficient Layer-wise LLM Fine-tuning for Revision Intention Prediction, https://arxiv.org/abs/2510.00268
• Run Su, Hao Fu, Shuai Zhou, and Yingao Fu, 1 Oct 2025, Integrating Offline Pre-Training with Online Fine-Tuning: A Reinforcement Learning Approach for Robot Social Navigation, https://arxiv.org/abs/2510.00466
• Gaotang Li, Ruizhong Qiu, Xiusi Chen, Heng Ji, Hanghang Tong, 1 Oct 2025, Beyond Log Likelihood: Probability-Based Objectives for Supervised Fine-Tuning across the Model Capability Continuum, https://arxiv.org/abs/2510.00526
• Roshan Kenia, Anfei Li, Rishabh Srivastava, Kaveri A. Thakoor, 1 Oct 2025, AI-CNet3D: An Anatomically-Informed Cross-Attention Network with Multi-Task Consistency Fine-tuning for 3D Glaucoma Classification, https://arxiv.org/abs/2510.00882
• Matteo Fuoli, Weihang Huang, Jeannette Littlemore, Sarah Turner, Ellen Wilding, 1 Oct 2025, Metaphor identification using large language models: A comparison of RAG, prompt engineering, and fine-tuning, https://arxiv.org/abs/2509.24866
• Matteo Cardoni, Sam Leroux, 24 Sep 2025, Predictive Coding-based Deep Neural Network Fine-tuning for Computationally Efficient Domain Adaptation, https://arxiv.org/abs/2509.20269
• Jingyi Wang, Zhongyuan Zhao, Qingtian Wang, Zexu Li, Yue Wang, Tony Q. S. Quek, 5 Sep 2025, A Federated Fine-Tuning Paradigm of Foundation Models in Heterogenous Wireless Networks, https://arxiv.org/abs/2509.19306
• Adrien Goldszal and Diego Calanzone and Vincent Taboga and Pierre-Luc Bacon, 23 Sep 2025, Discovery of Sustainable Refrigerants through Physics-Informed RL Fine-Tuning of Sequence Models, https://arxiv.org/abs/2509.19588
• Babak Barazandeh, Subhabrata Majumdar, Om Rajyaguru, George Michailidis, 23 Sep 2025, Localized LoRA: A Structured Low-Rank Approximation for Efficient Fine-Tuning, https://arxiv.org/abs/2506.00236
• Hui Yi Leong, Yi Fan Gao, Ji Shuai, Yang Zhang, Uktu Pamuksuz, 24 Sep 2025, Efficient Fine-Tuning of Large Language Models for Automated Medical Documentation, https://arxiv.org/abs/2409.09324
• Yingming Zheng, Hanqi Li, Kai Yu and Lu Chen, 24 Sep 2025, When Long Helps Short: How Context Length in Supervised Fine-tuning Affects Behavior of Large Language Models, https://arxiv.org/abs/2509.18762
• Wenpin Tang and Fuzhong Zhou, 23 Sep 2025, Fine-tuning of diffusion models via stochastic control: entropy regularization and beyond, https://arxiv.org/abs/2403.06279
• Yilang Zhang, Xiaodong Yang, Yiwei Cai, Georgios B. Giannakis, 27 Oct 2025, ScaLoRA: Optimally Scaled Low-Rank Adaptation for Efficient High-Rank Fine-Tuning, https://arxiv.org/abs/2510.23818
• Kanghyun Choi, Hyeyoon Lee, SunJong Park, Dain Kwon, Jinho Lee, 28 Oct 2025, FALQON: Accelerating LoRA Fine-tuning with Low-Bit Floating-Point Arithmetic, https://arxiv.org/abs/2510.24061
• Marton Szep, Daniel Rueckert, R\"udiger von Eisenhart-Rothe, Florian Hinterwimmer, 14 Nov 2024, Fine-tuning Large Language Models with Limited Data: A Survey and Practical Guide, https://arxiv.org/abs/2411.09539
• Yueqi Song, Ketan Ramaneti, Zaid Sheikh, Ziru Chen, Boyu Gou, Tianbao Xie, Yiheng Xu, Danyang Zhang, Apurva Gandhi, Fan Yang, Joseph Liu, Tianyue Ou, Zhihao Yuan, Frank Xu, Shuyan Zhou, Xingyao Wang, Xiang Yue, Tao Yu, Huan Sun, Yu Su, Graham Neubig, 28 Oct 2025, Agent Data Protocol: Unifying Datasets for Diverse, Effective Fine-tuning of LLM Agents, https://arxiv.org/abs/2510.24702
• Shufan Shen, Junshu Sun, Shuhui Wang, Qingming Huang, 28 Oct 2025, Kernelized Sparse Fine-Tuning with Bi-level Parameter Competition for Vision Models, https://arxiv.org/abs/2510.24037
• Amit Peleg, Naman Deep Singh, Matthias Hein, 28 Oct 2025, Advancing Compositional Awareness in CLIP with Efficient Fine-Tuning, https://arxiv.org/abs/2505.24424
• Yifan Sun, Jingyan Shen, Yibin Wang, Tianyu Chen, Zhendong Wang, Mingyuan Zhou, Huan Zhang, 28 Oct 2025, Improving Data Efficiency for LLM Reinforcement Fine-tuning Through Difficulty-targeted Online Data Selection and Rollout Replay, https://arxiv.org/abs/2506.05316
• Nathan Paull, 28 Oct 2025, CustomIR: Unsupervised Fine-Tuning of Dense Embeddings for Known Document Corpora, https://arxiv.org/abs/2510.21729
• Subhojyoti Mukherjee, Viet Dac Lai, Raghavendra Addanki, Ryan Rossi, Seunghyun Yoon, Trung Bui, Anup Rao, Jayakumar Subramanian, Branislav Kveton, 27 Oct 2025, Offline RL by Reward-Weighted Fine-Tuning for Conversation Optimization, https://arxiv.org/abs/2506.06964
• Chen Wang, Zhaochun Li, Jionghao Bai, Yuzhi Zhang, Shisheng Cui, Zhou Zhao, Yue Wang, 23 Oct 2025, Arbitrary Entropy Policy Optimization: Entropy Is Controllable in Reinforcement Fine-tuning, https://arxiv.org/abs/2510.08141
• Himanshu Beniwal, Youngwoo Kim, Maarten Sap, Soham Dan, Thomas Hartvigsen, 23 Oct 2025, Breaking mBad! Supervised Fine-tuning for Cross-Lingual Detoxification, https://arxiv.org/abs/2505.16722
• Saransh Gupta, Umesh Deshpande, Travis Janssen, Swami Sundararaman, 23 Oct 2025, Symbiosis: Multi-Adapter Inference and Fine-Tuning, https://arxiv.org/abs/2507.03220
• Igli Begolli, Meltem Aksoy, Daniel Neider, 23 Oct 2025, Fine-Tuning Multilingual Language Models for Code Review: An Empirical Study on Industrial C# Projects, https://arxiv.org/abs/2507.19271
• M. H. I. Abdalla, Zhipin Wang, Christian Frey, Steffen Eger, Josif Grabocka, 21 Oct 2025, Zhyper: Factorized Hypernetworks for Conditioned LLM Fine-Tuning, https://arxiv.org/abs/2510.19733 replaced
• Tuowei Wang, Kun Li, Zixu Hao, Donglin Bai, Ju Ren, Yaoxue Zhang, Ting Cao, Mao Yang, 12 Oct 2025, Long Exposure: Accelerating Parameter-Efficient Fine-Tuning for LLMs under Shadowy Sparsity, https://arxiv.org/abs/2510.15964
• Changsheng Wang, Xin Chen, Sijia Liu, Ke Ding, 15 Oct 2025, Breaking Memorization Barriers in LLM Code Fine-Tuning via Information Bottleneck for Improved Generalization, https://arxiv.org/abs/2510.16022
• Heming Zou, Yixiu Mao, Yun Qu, Qi Wang, Xiangyang Ji, 19 Oct 2025, Utility-Diversity Aware Online Batch Selection for LLM Supervised Fine-tuning, https://arxiv.org/abs/2510.16882
• Sarah Egler, John Schulman, Nicholas Carlini, 17 Oct 2025, Detecting Adversarial Fine-tuning with Auditing Agents, https://arxiv.org/abs/2510.16255
• Mingzheng Zhang, Jinfeng Gao, Dan Xu, Jiangrui Yu, Yuhan Qiao, Lan Chen, Jin Tang, and Xiao Wang, 19 Oct 2025, EMRRG: Efficient Fine-Tuning Pre-trained X-ray Mamba Networks for Radiology Report Generation, https://arxiv.org/abs/2510.16776
• Akif Islam and Mohd Ruhul Ameen, 19 Oct 2025, Parameter-Efficient Fine-Tuning for Low-Resource Languages: A Comparative Study of LLMs for Bengali Hate Speech Detection, https://arxiv.org/abs/2510.16985
• Yupeng Chen, Senmiao Wang, Yushun Zhang, Zhihang Lin, Haozhe Zhang, Weijian Sun, Tian Ding, Ruoyu Sun, 18 Oct 2025, MoFO: Momentum-Filtered Optimizer for Mitigating Forgetting in LLM Fine-Tuning, https://arxiv.org/abs/2407.20999
• Fabian Paischer, Lukas Hauzenberger, Thomas Schmied, Benedikt Alkin, Marc Peter Deisenroth, Sepp Hochreiter, 20 Oct 2025, Parameter Efficient Fine-tuning via Explained Variance Adaptation, https://arxiv.org/abs/2410.07170
• Mingyang Liu, Gabriele Farina, Asuman Ozdaglar, 19 Oct 2025, UFT: Unifying Supervised and Reinforcement Fine-Tuning, https://arxiv.org/abs/2505.16984
• Binghao Huang, Jie Xu, Iretiayo Akinola, Wei Yang, Balakumar Sundaralingam, Rowland O'Flaherty, Dieter Fox, Xiaolong Wang, Arsalan Mousavian, Yu-Wei Chao, Yunzhu Li, 18 Oct 2025, VT-Refine: Learning Bimanual Assembly with Visuo-Tactile Feedback via Simulation Fine-Tuning, https://arxiv.org/abs/2510.14930
• Lovely Yeswanth Panchumarthi, Saurabh Kataria, Yi Wu, Xiao Hu, Alex Fedorov, Hyunjung Gloria Kwak, 20 Sep 2025, FairTune: A Bias-Aware Fine-Tuning Framework Towards Fair Heart Rate Prediction from PPG, https://arxiv.org/abs/2509.16491
• Junjie Ye, Yuming Yang, Yang Nan, Shuo Li, Qi Zhang, Tao Gui, Xuanjing Huang, Peng Wang, Zhongchao Shi, Jianping Fan, 20 Sep 2025, Analyzing the Effects of Supervised Fine-Tuning on Model Knowledge from Token and Parameter Levels, https://arxiv.org/abs/2509.16596
• Salha Alyami, Amani Jamal, Areej Alhothali, 20 Sep 2025, Domain-Adaptive Pre-Training for Arabic Aspect-Based Sentiment Analysis: A Comparative Study of Domain Adaptation and Fine-Tuning Strategies, https://arxiv.org/abs/2509.16788
• Talha Tahir, 20 Sep 2025, Fine-Tuning Open-Weight Language Models to Deliver Cognitive Behavioral Therapy for Depression: A Feasibility Study, https://arxiv.org/abs/2412.00251
• Lei Gao, Amir Ziashahabi, Yue Niu, Salman Avestimehr, Murali Annavaram, 20 Sep 2025, MobiZO: Enabling Efficient LLM Fine-Tuning at the Edge via Inference Engines, https://arxiv.org/abs/2409.15520
• Yilang Zhang, Bingcong Li, Georgios B. Giannakis, 21 Sep 2025, RefLoRA: Refactored Low-Rank Adaptation for Efficient Fine-Tuning of Large Models, https://arxiv.org/abs/2505.18877
• Yang Wang, Qibin Liang, Chenghao Xiao, Yizhi Li, Noura Al Moubayed, Chenghua Lin, 22 Sep 2025, Audio Contrastive-based Fine-tuning: Decoupling Representation Learning and Classification, https://arxiv.org/abs/2309.11895
• Yujie Xiao, Gongzhen Tang, Wenhui Liu, Jun Li, Guangkun Nie, Zhuoran Kan, Deyun Zhang, Qinghao Zhao, Shenda Hong, 25 Oct 2025, AnyECG-Lab: An Exploration Study of Fine-tuning an ECG Foundation Model to Estimate Laboratory Values from Single-Lead ECG Signals, https://arxiv.org/abs/2510.22301
• Anh Pham, Mihir Thalanki, Michael Sun, Aditya Chaloo, Ankita Gupta, Tian Xia, Aditya Mate, Ehimwenma Nosakhare, Soundararajan Srinivasan, 23 Oct 2025, Preventing Catastrophic Forgetting: Behavior-Aware Sampling for Safer Language Model Fine-Tuning, https://arxiv.org/abs/2510.21885
• Andrei Baroian, 25 Oct 2025, Supervised Fine-Tuning or In-Context Learning? Evaluating LLMs for Clinical NER, https://arxiv.org/abs/2510.22285
• Noshitha Padma Pratyusha Juttu, Sahithi Singireddy, Sravani Gona and Sujal Timilsina, 26 Oct 2025, Text to Trust: Evaluating Fine-Tuning and LoRA Trade-offs in Language Models for Unfair Terms of Service Detection, https://arxiv.org/abs/2510.22531
• Wanxin Tian, Shijie Zhang, Kevin Zhang, Xiaowei Chi, Chunkai Fan, Junyu Lu, Yulin Luo, Qiang Zhou, Yiming Zhao, Ning Liu, Siyu Lin, Zhiyuan Qin, Xiaozhu Ju, Shanghang Zhang, Jian Tang, 27 Oct 2025, SEEA-R1: Tree-Structured Reinforcement Fine-Tuning for Self-Evolving Embodied Agents, https://arxiv.org/abs/2506.21669
• Reza Shirkavand, Peiran Yu, Qi He, Heng Huang, 27 Oct 2025, Bilevel ZOFO: Bridging Parameter-Efficient and Zeroth-Order Techniques for Efficient LLM Fine-Tuning and Meta-Training, https://arxiv.org/abs/2502.03604
• Shivam Ratnakar, Abhiroop Talasila, Raghav Chamadiya, Nikhil Agarwal, Vinayak K Doifode, 26 Oct 2025, Beyond QA Pairs: Assessing Parameter-Efficient Fine-Tuning for Fact Embedding in LLMs, https://arxiv.org/abs/2503.01131
• Nicolas Menet, Aleksandar Terzi\'c, Andreas Krause, Abbas Rahimi, 15 Oct 2025, Thompson Sampling via Fine-Tuning of LLMs, https://arxiv.org/abs/2510.13328
• Shingo Ayabe, Hiroshi Kera, Kazuhiko Kawamoto, 15 Oct 2025, Adversarial Fine-tuning in Offline-to-Online Reinforcement Learning for Robust Robot Control, https://arxiv.org/abs/2510.13358
• Jingyao Wang, Wenwen Qiang, Zeen Song, Changwen Zheng, Hui Xiong, 15 Oct 2025, Learning to Think: Information-Theoretic Reinforcement Fine-Tuning for LLMs, https://arxiv.org/abs/2505.10425
• Zilun Zhang, Zian Guan, Tiancheng Zhao, Haozhan Shen, Tianyu Li, Yuxiang Cai, Zhonggen Su, Zhaojun Liu, Jianwei Yin, Xiang Li, 15 Oct 2025, Geo-R1: Improving Few-Shot Geospatial Referring Expression Understanding with Reinforcement Fine-Tuning, https://arxiv.org/abs/2509.21976
• Guanghao Zhu, Zhitian Hou, Zeyu Liu, Zhijie Sang, Congkai Xie, Hongxia Yang, 26 Sep 2025, InfiMed-Foundation: Pioneering Advanced Multimodal Medical Models with Compute-Efficient Pre-Training and Multi-Stage Fine-Tuning, https://arxiv.org/abs/2509.22261
• Feng Yu and Jia Hu and Geyong Min, 25 Sep 2025, Blockwise Hadamard high-Rank Adaptation for Parameter-Efficient LLM Fine-Tuning, https://arxiv.org/abs/2509.21637
• Shilei Cao, Hehai Lin, Jiashun Cheng, Yang Liu, Guowen Li, Xuehe Wang, Juepeng Zheng, Haoyuan Liang, Meng Jin, Chengwei Qin, Hong Cheng, Haohuan Fu, 26 Sep 2025, Task-Adaptive Parameter-Efficient Fine-Tuning for Weather Foundation Models, https://arxiv.org/abs/2509.22020
• Aayush Mishra, Daniel Khashabi, Anqi Liu, 26 Sep 2025, IA2: Alignment with ICL Activations Improves Supervised Fine-Tuning, https://arxiv.org/abs/2509.22621
• Fei Wu, Jia Hu, Geyong Min, Shiqiang Wang, 26 Sep 2025, Efficient Orthogonal Fine-Tuning with Principal Subspace Adaptation, https://arxiv.org/abs/2505.11235
• Jaedong Hwang, Brian Cheung, Zhang-Wei Hong, Akhilan Boopathy, Pulkit Agrawal, Ila Fiete, 26 Sep 2025, Large Pre-Training Datasets Don't Always Guarantee Robustness after Fine-Tuning, https://arxiv.org/abs/2410.21582
• Zhihao Zhang, Qiaole Dong, Qi Zhang, Jun Zhao, Enyu Zhou, Zhiheng Xi, Senjie Jin, Xiaoran Fan, Yuhao Zhou, Mingqi Wu, Yanwei Fu, Tao Ji, Tao Gui, Xuanjing Huang and Kai Chen, 26 Sep 2025, Why Reinforcement Fine-Tuning Enables MLLMs Preserve Prior Knowledge Better: A Data Perspective, https://arxiv.org/abs/2506.23508
• Samyak Jhaveri, Vanessa Klotzmann, Crista Lopes, 26 Sep 2025, ACCeLLiuM: Supervised Fine-Tuning for Automated OpenACC Pragma Generation, https://arxiv.org/abs/2509.20380
• Sajjad Ghiasvand, Mahnoosh Alizadeh, Ramtin Pedarsani, 26 Sep 2025, pFedMMA: Personalized Federated Fine-Tuning with Multi-Modal Adapter for Vision-Language Models, https://arxiv.org/abs/2507.05394
• Aryan Golbaghi, Shuo Zhou, 8 Oct 2025, Enhancing Speech Emotion Recognition via Fine-Tuning Pre-Trained Models and Hyper-Parameter Optimisation, https://arxiv.org/abs/2510.07052
• Gautham Govind Anil, Shaan Ul Haque, Nithish Kannen, Dheeraj Nagaraj, Sanjay Shakkottai, Karthikeyan Shanmugam, 3 Oct 2025, Fine-Tuning Diffusion Models via Intermediate Distribution Shaping, https://arxiv.org/abs/2510.02692
• Derek Shi, Ruben Glatt, Christine Klymko, Shubham Mohole, Hongjun Choi, Shashank Kushwaha, Sam Sakla, Felipe Leno da Silva, 2 Oct 2025, Oracle-RLAIF: An Improved Fine-Tuning Framework for Multi-modal Video Models through Reinforcement Learning from Ranking Feedback, https://arxiv.org/abs/2510.02561
• Daphne Tsolissou, Theofanis Ganitidis, Konstantinos Mitsis, Stergios CHristodoulidis, Maria Vakalopoulou, Konstantina Nikita, 3 Oct 2025, Multimodal Carotid Risk Stratification with Large Vision-Language Models: Benchmarking, Fine-Tuning, and Clinical Insights, https://arxiv.org/abs/2510.02922
• Jannik Graebner, Ryne Beeson, 2 Oct 2025, Self-supervised diffusion model fine-tuning for costate initialization using Markov chain Monte Carlo, https://arxiv.org/abs/2510.02527
• He Zhu, Junyou Su, Peng Lai, Ren Ma, Wenjia Zhang, Linyi Yang, Guanhua Chen, 3 Oct 2025, Anchored Supervised Fine-Tuning, https://arxiv.org/abs/2509.23753
• Huan Song, Deeksha Razdan, Yiyue Qian, Arijit Ghosh Chowdhury, Parth Patwa, Aman Chadha, Shinan Zhang, Sharlina Keshava, Hannah Marlowe, 20 Oct 2025, Learning from Generalization Patterns: An Evaluation-Driven Approach to Enhanced Data Augmentation for Fine-Tuning Small Language Models, https://arxiv.org/abs/2510.18143
• Xiaohan Qin, Xiaoxing Wang, Ning Liao, Cancheng Zhang, Xiangdong Zhang, Mingquan Feng, Jingzhi Wang, Junchi Yan, 21 Oct 2025, ssToken: Self-modulated and Semantic-aware Token Selection for LLM Fine-tuning, https://arxiv.org/abs/2510.18250
• Jiajun Fan, Tong Wei, Chaoran Cheng, Yuxin Chen, Ge Liu, 20 Oct 2025, Adaptive Divergence Regularized Policy Optimization for Fine-tuning Generative Models, https://arxiv.org/abs/2510.18053
• Jiajun Fan, Chaoran Cheng, Shuaike Shen, Xiangxin Zhou, Ge Liu, 20 Oct 2025, Fine-tuning Flow Matching Generative Models with Intermediate Feedback, https://arxiv.org/abs/2510.18072
• Zhendong Mi, Qitao Tan, Grace Li Zhang, Zhaozhuo Xu, Geng Yuan, Shaoyi Huang, 21 Oct 2025, Towards Fast LLM Fine-tuning through Zeroth-Order Optimization with Projected Gradient-Aligned Perturbations, https://arxiv.org/abs/2510.18228
• Mariano Rivera and Angello Hoyos, 20 Oct 2025, COLORA: Efficient Fine-Tuning for Convolutional Models with a Study Case on Optical Coherence Tomography Image Classification, https://arxiv.org/abs/2505.18315
• Mingze Yuan, Pengfei Jin, Na Li, Quanzheng Li, 24 Sep 2025, PIRF: Physics-Informed Reward Fine-Tuning for Diffusion Models, https://arxiv.org/abs/2509.20570
• Honglin Zhang, Qianyue Hao, Fengli Xu, Yong Li, 25 Sep 2025, Reinforcement Learning Fine-Tuning Enhances Activation Intensity and Diversity in the Internal Circuitry of LLMs, https://arxiv.org/abs/2509.21044
• Yongda Yu, Guohao Shi, Xianwei Wu, Haochuan He, XueMing Gu, Qianqian Zhao, Kui Liu, Qiushi Wang, Zhao Tian, Haifeng Shen, Guoping Rong, 25 Sep 2025, Fine-Tuning LLMs to Analyze Multiple Dimensions of Code Review: A Maximum Entropy Regulated Long Chain-of-Thought Approach, https://arxiv.org/abs/2509.21170
• Zeyu Huang, Tianhao Cheng, Zihan Qiu, Zili Wang, Yinghui Xu, Edoardo M. Ponti, Ivan Titov, 24 Sep 2025, Blending Supervised and Reinforcement Fine-Tuning with Prefix Sampling, https://arxiv.org/abs/2507.01679
• Song Lai, Haohan Zhao, Rong Feng, Changyi Ma, Wenzhuo Liu, Hongbo Zhao, Xi Lin, Dong Yi, Min Xie, Qingfu Zhang, Hongbin Liu, Gaofeng Meng, Fei Zhu, 25 Sep 2025, Reinforcement Fine-Tuning Naturally Mitigates Forgetting in Continual Post-Training, https://arxiv.org/abs/2507.05386
• Hangwei Zhang, Chun Kang, Yan Wang, Difan Zou, 27 Sep 2025, F-Adapter: Frequency-Adaptive Parameter-Efficient Fine-Tuning in Scientific Machine Learning, https://arxiv.org/abs/2509.23173
• Jonas Ngnaw\'e, Maxime Heuillet, Sabyasachi Sahoo, Yann Pequignot, Ola Ahmad, Audrey Durand, Fr\'ed\'eric Precioso, Christian Gagn\'e, 27 Sep 2025, Robust Fine-Tuning from Non-Robust Pretrained Models: Mitigating Suboptimal Transfer With Adversarial Scheduling, https://arxiv.org/abs/2509.23325
• Jiang-Xin Shi, Wen-Da Wei, Jin-Fei Qi, Xuanyu Chen, Tong Wei, Yu-Feng Li, 27 Sep 2025, Memory-Efficient Fine-Tuning via Low-Rank Activation Compression, https://arxiv.org/abs/2509.23472
• Zhixin Zhang, Zeming Wei, Meng Sun, 28 Sep 2025, Dynamic Orthogonal Continual Fine-tuning for Mitigating Catastrophic Forgettings, https://arxiv.org/abs/2509.23893
• Xin Qiu, Yulu Gan, Conor F. Hayes, Qiyao Liang, Elliot Meyerson, Babak Hodjat, Risto Miikkulainen, 29 Sep 2025, Evolution Strategies at Scale: LLM Fine-Tuning Beyond Reinforcement Learning, https://arxiv.org/abs/2509.24372
• David Gonz\'alez Mart\'inez, 29 Sep 2025, BALF: Budgeted Activation-Aware Low-Rank Factorization for Fine-Tuning-Free Model Compression, https://arxiv.org/abs/2509.25136
• Sophia Tang, Yuchen Zhu, Molei Tao, Pranam Chatterjee, 29 Sep 2025, TR2-D2: Tree Search Guided Trajectory-Aware Fine-Tuning for Discrete Diffusion, https://arxiv.org/abs/2509.25171
• Jaehan Kim, Minkyoo Song, Seungwon Shin, Sooel Son, 26 Sep 2025, Defending MoE LLMs against Harmful Fine-Tuning via Safety Routing Alignment, https://arxiv.org/abs/2509.22745
• Nayeong Kim, Seong Joon Oh, Suha Kwak, 28 Sep 2025, GroupCoOp: Group-robust Fine-tuning via Group Prompt Learning, https://arxiv.org/abs/2509.23781
• Yiwei Chen, Yuguang Yao, Yihua Zhang, Bingquan Shen, Gaowen Liu, Sijia Liu, 28 Sep 2025, Safety Mirage: How Spurious Correlations Undermine VLM Safety Fine-Tuning and Can Be Mitigated by Machine Unlearning, https://arxiv.org/abs/2503.11832
• Ruijia Niu, Dongxia Wu, Rose Yu, Yi-An Ma, 29 Sep 2025, Functional-level Uncertainty Quantification for Calibrated Fine-tuning on LLMs, https://arxiv.org/abs/2410.06431
• Wenzhi Fang, Dong-Jun Han, Liangqi Yuan, Seyyedali Hosseinalipour, Christopher G. Brinton, 28 Sep 2025, Federated Sketching LoRA: A Flexible Framework for Heterogeneous Collaborative Fine-Tuning of LLMs, https://arxiv.org/abs/2501.19389
• Ningyuan Yang, Jiaxuan Gao, Feng Gao, Yi Wu, Chao Yu, 28 Sep 2025, Fine-tuning Diffusion Policies with Backpropagation Through Diffusion Timesteps, https://arxiv.org/abs/2505.10482
• Xuchen Pan, Yanxi Chen, Yushuo Chen, Yuchang Sun, Daoyuan Chen, Wenhao Zhang, Yuexiang Xie, Yilun Huang, Yilei Zhang, Dawei Gao, Weijie Shi, Yaliang Li, Bolin Ding, Jingren Zhou, 29 Sep 2025, Trinity-RFT: A General-Purpose and Unified Framework for Reinforcement Fine-Tuning of Large Language Models, https://arxiv.org/abs/2505.17826
• Junyu Chen, Junzhuo Li, Zhen Peng, Wenjie Wang, Yuxiang Ren, Long Shi, Xuming Hu, 28 Sep 2025, LoTA-QAF: Lossless Ternary Adaptation for Quantization-Aware Fine-Tuning, https://arxiv.org/abs/2505.18724
• Tao Ren, Zishi Zhang, Jingyang Jiang, Zehao Li, Shentao Qin, Yi Zheng, Guanghao Li, Qianyou Sun, Yan Li, Jiafeng Liang, Xinping Li, Yijie Peng, 28 Sep 2025, Half-order Fine-Tuning for Diffusion Model: A Recursive Likelihood Ratio Optimizer, https://arxiv.org/abs/2502.00639
• Chenxing Wei, Yao Shu, Mingwen Ou, Ying Tiffany He, Fei Richard Yu, 27 Sep 2025, PAFT: Prompt-Agnostic Fine-Tuning, https://arxiv.org/abs/2502.12859
• Core Francisco Park, Zechen Zhang, Hidenori Tanaka, 27 Sep 2025, $\textit{New News}$: System-2 Fine-tuning for Robust Integration of New Knowledge, https://arxiv.org/abs/2505.01812
• Hakaze Cho, Peng Luo, Mariko Kato, Rin Kaenbyou, Naoya Inoue, 27 Sep 2025, Mechanistic Fine-tuning for In-context Learning, https://arxiv.org/abs/2505.14233
• Yuansheng Ni, Ping Nie, Kai Zou, Xiang Yue, Wenhu Chen, 29 Sep 2025, VisCoder: Fine-Tuning LLMs for Executable Python Visualization Code Generation, https://arxiv.org/abs/2506.03930
• Zengjue Chen, Runliang Niu, He Kong, Qi Wang, Qianli Xing, Zipei Fan, 27 Sep 2025, TGRPO :Fine-tuning Vision-Language-Action Model via Trajectory-wise Group Relative Policy Optimization, https://arxiv.org/abs/2506.08440
• Lee Qi Zun, Mohamad Zulhilmi Bin Abdul Halim, Goh Man Fye, 17 Oct 2025, Fine-Tuning MedGemma for Clinical Captioning to Enhance Multimodal RAG over Malaysia CPGs, https://arxiv.org/abs/2510.15418
• Gokul Swamy, Sanjiban Choudhury, Wen Sun, Zhiwei Steven Wu, J. Andrew Bagnell, 17 Oct 2025, All Roads Lead to Likelihood: The Value of Reinforcement Learning in Fine-Tuning, https://arxiv.org/abs/2503.01067
• Congzheng Song, Xinyu Tang, 3 Oct 2025, Memory-Efficient Backpropagation for Fine-Tuning LLMs on Resource-Constrained Mobile Devices, https://arxiv.org/abs/2510.03425
• Yongfu Xue, 4 Oct 2025, Optimizing Fine-Tuning through Advanced Initialization Strategies for Low-Rank Adaptation, https://arxiv.org/abs/2510.03731
• Junde Xu, Yapin Shi, Lijun Lang, Taoyong Cui, Zhiming Zhang, Guangyong Chen, Jiezhong Qiu, Pheng-Ann Heng, 3 Oct 2025, InstructPLM-mu: 1-Hour Fine-Tuning of ESM2 Beats ESM3 in Protein Mutation Predictions, https://arxiv.org/abs/2510.03370
• Imran Mansha, 6 Oct 2025, Resource-Efficient Fine-Tuning of LLaMA-3.2-3B for Medical Chain-of-Thought Reasoning, https://arxiv.org/abs/2510.05003
• Kuang Yuan, Yang Gao, Xilin Li, Xinhao Mei, Syavosh Zadissa, Tarun Pruthi, Saeed Bagheri Sereshki, 4 Oct 2025, Lightweight and Generalizable Acoustic Scene Representations via Contrastive Fine-Tuning and Distillation, https://arxiv.org/abs/2510.03728
• Lu Ma, Hao Liang, Meiyi Qiang, Lexiang Tang, Xiaochen Ma, Zhen Hao Wong, Junbo Niu, Chengyu Shen, Runming He, Yanhao Li, Bin Cui, Wentao Zhang, 4 Oct 2025, Learning What Reinforcement Learning Can't: Interleaved Online Fine-Tuning for Hardest Questions, https://arxiv.org/abs/2506.07527
• Snehal Raj, Brian Coyle, 5 Oct 2025, QuIC: Quantum-Inspired Compound Adapters for Parameter Efficient Fine-Tuning, https://arxiv.org/abs/2502.06916
• Raghav Singhal, Kaustubh Ponkshe, Rohit Vartak, Lav R. Varshney, Praneeth Vepakomma, 4 Oct 2025, Fed-SB: A Silver Bullet for Extreme Communication Efficiency and Performance in (Private) Federated LoRA Fine-Tuning, https://arxiv.org/abs/2502.15436
• Kaustubh Ponkshe, Shaan Shah, Raghav Singhal, Praneeth Vepakomma, 4 Oct 2025, Safety Subspaces are Not Linearly Distinct: A Fine-Tuning Case Study, https://arxiv.org/abs/2505.14185
• Huajie Tan, Yuheng Ji, Xiaoshuai Hao, Xiansheng Chen, Pengwei Wang, Zhongyuan Wang, Shanghang Zhang, 5 Oct 2025, Reason-RFT: Reinforcement Fine-Tuning for Visual Reasoning of Vision Language Models, https://arxiv.org/abs/2503.20752
• Hung-Ying Chu, Shao-Yu Wei, Guan-Wei Chen, Tzu-Wei Hung, ChengYang Tsai and Yu-Cheng Lin, 4 Oct 2025, HNote: Extending YNote with Hexadecimal Encoding for Fine-Tuning LLMs in Music Modeling, https://arxiv.org/abs/2509.25694
• Nirmal Elamon, Rouzbeh Davoudi, 3 Oct 2025, Beyond CNNs: Efficient Fine-Tuning of Multi-Modal LLMs for Object Detection on Low-Data Regimes, https://arxiv.org/abs/2510.08589
• Sybelle Goedicke-Fritz (1), Michelle Bous (1), Annika Engel (2), Matthias Flotho (2 and 5), Pascal Hirsch (2), Hannah Wittig (1), Dino Milanovic (2), Dominik Mohr (1), Mathias Kaspar (6), Sogand Nemat (3), Dorothea Kerner (3), Arno B\"ucker (3), Andreas Keller (2 and 5 and 7), Sascha Meyer (4), Michael Zemlin (1), Philipp Flotho (2 and 5) ((1) Department of General Pediatrics and Neonatology, Saarland University, Campus Homburg, Homburg/Saar, Germany, (2) Chair for Clinical Bioinformatics, Saarland Informatics Campus, Saarland University, Saarbr\"ucken, Germany, (3) Department of Radiology, and Interventional Radiology, University Hospital of Saarland, Homburg, Germany, (4) Clinical Centre Karlsruhe, Franz-Lust Clinic for Paediatrics, Karlsruhe, Germany, (5) Helmholtz Institute for Pharmaceutical Research Saarland (HIPS), Saarland University Campus, Germany, (6) Digital Medicine, University Hospital of Augsburg, Augsburg, Germany, (7) Pharma Science Hub (PSH), Saarland University Campus, Germany), 10 Oct 2025, Site-Level Fine-Tuning with Progressive Layer Freezing: Towards Robust Prediction of Bronchopulmonary Dysplasia from Day-1 Chest Radiographs in Extremely Preterm Infants, https://arxiv.org/abs/2507.12269
• Aymane El Firdoussi, El Mahdi Chayti, Mohamed El Amine Seddik, Martin Jaggi, 24 Oct 2025, $\alpha$-LoRA: Effective Fine-Tuning via Base Model Rescaling, https://arxiv.org/abs/2510.21345
• Jan Wehner, Mario Fritz, 24 Oct 2025, Probe-based Fine-tuning for Reducing Toxicity, https://arxiv.org/abs/2510.21531
• Mingyang Lyu, Yinqian Sun, Erliang Lin, Huangrui Li, Ruolin Chen, Feifei Zhao, Yi Zeng, 11 Oct 2025, Reinforcement Fine-Tuning of Flow-Matching Policies for Vision-Language-Action Models, https://arxiv.org/abs/2510.09976
• Jianzhe Zhao, Hailin Zhu, Yu Zhang, Ziqi Chen, Guibing Guo, 13 Oct 2025, FedLoRA-Optimizer: Federated LoRA Fine-Tuning with Global and Local Optimization in Heterogeneous Data Scenarios, https://arxiv.org/abs/2510.11274
• Guozhi Liu, Qi Mu, Tiansheng Huang, Xinhua Wang, Li Shen, Weiwei Lin, Zhang Li, 11 Oct 2025, Pharmacist: Safety Alignment Data Curation for Large Language Models against Harmful Fine-tuning, https://arxiv.org/abs/2510.10085
• Ma\"el Macuglia, Paul Friedrich, Giorgia Ramponi, 13 Oct 2025, Fine-tuning Behavioral Cloning Policies with Preference-Based Reinforcement Learning, https://arxiv.org/abs/2509.26605
• Haifeng Wen, Hong Xing, Osvaldo Simeone, 11 Oct 2025, Pre-Training and Personalized Fine-Tuning via Over-the-Air Federated Meta-Learning: Convergence-Generalization Trade-Offs, https://arxiv.org/abs/2406.11569
• Jieming Bian, Lei Wang, Letian Zhang, Jie Xu, 12 Oct 2025, LoRA-FAIR: Federated LoRA Fine-Tuning with Aggregation and Initialization Refinement, https://arxiv.org/abs/2411.14961
• Yongqiang Yao, Jingru Tan, Kaihuan Liang, Feizhao Zhang, Jiahao Hu, Shuo Wu, Yazhe Niu, Ruihao Gong, Dahua Lin, Ningyi Xu, 13 Oct 2025, Hierarchical Balance Packing: Towards Efficient Supervised Fine-tuning for Long-Context LLM, https://arxiv.org/abs/2503.07680
• Changsheng Wang, Yihua Zhang, Jinghan Jia, Parikshit Ram, Dennis Wei, Yuguang Yao, Soumyadeep Pal, Nathalie Baracaldo, Sijia Liu, 10 Oct 2025, Invariance Makes LLM Unlearning Resilient Even to Unanticipated Downstream Fine-Tuning, https://arxiv.org/abs/2506.01339
• Andrey Goncharov, Daniil Vyazhev, Petr Sychev, Edvard Khalafyan, Alexey Zaytsev, 11 Oct 2025, Complexity-aware fine-tuning, https://arxiv.org/abs/2506.21220
• Majid Jaberi-Douraki, Hossein Sholehrasa, Xuan Xu, Remya Ampadi Ramachandran, 9 Oct 2025, HySim-LLM: Embedding-Weighted Fine-Tuning Bounds and Manifold Denoising for Domain-Adapted LLMs, https://arxiv.org/abs/2510.07796
• Yicheng Zhang, Zhen Qin, Zhaomin Wu, Jian Hou, Shuiguang Deng, 9 Oct 2025, Personalized Federated Fine-Tuning for LLMs via Data-Driven Heterogeneous Model Architectures, https://arxiv.org/abs/2411.19128
• Vignesh Ethiraj, Ashwath David, Sidhanth Menon, Divya Vijay, Vidhyakshaya Kannan, 9 Oct 2025, T-VEC: A Telecom-Specific Vectorization Model with Enhanced Semantic Understanding via Deep Triplet Loss Fine-Tuning, https://arxiv.org/abs/2504.16460
• Hong-Jie Dai, Zheng-Hao Li, An-Tai Lu, Bo-Tsz Shain, Ming-Ta Li, Tatheer Hussain Mir, Kuang-Te Wang, Min-I Su, Pei-Kang Liu, Ming-Ju Tsai, 23 Sep 2025, Model selection meets clinical semantics: Optimizing ICD-10-CM prediction via LLM-as-Judge evaluation, redundancy-aware sampling, and section-aware fine-tuning, https://arxiv.org/abs/2509.18846
• Xiao Han, Zimo Zhao, Wanyu Wang, Maolin Wang, Zitao Liu, Yi Chang, Xiangyu Zhao, 23 Sep 2025, Data Efficient Adaptation in Large Language Models via Continuous Low-Rank Fine-Tuning, https://arxiv.org/abs/2509.18942
• Yu Chen, Yifei Han, Long Zhang, Yue Du, Bin Li, 23 Sep 2025, TsqLoRA: Towards Sensitivity and Quality Low-Rank Adaptation for Efficient Fine-Tuning, https://arxiv.org/abs/2509.18585
• Yueyan Li, Wenhao Gao, Caixia Yuan, Xiaojie Wang, 23 Sep 2025, Fine-Tuning is Subgraph Search: A New Lens on Learning Dynamics, https://arxiv.org/abs/2502.06106
• Zhi Zhang, Yixian Shen, Congfeng Cao, Ekaterina Shutova, 21 Oct 2025, NeuroAda: Activating Each Neuron's Potential for Parameter-Efficient Fine-Tuning, https://arxiv.org/abs/2510.18940
• Peng Wang and Minghao Gu and Qiang Huang, 22 Oct 2025, Feature Space Adaptation for Robust Model Fine-Tuning, https://arxiv.org/abs/2510.19155
• A\"el Qu\'elennec, Nour Hezbri, Pavlo Mozharovskyi, Van-Tam Nguyen, Enzo Tartaglione, 22 Oct 2025, Study of Training Dynamics for Memory-Constrained Fine-Tuning, https://arxiv.org/abs/2510.19675
• Reece Shuttleworth, Jacob Andreas, Antonio Torralba, Pratyusha Sharma, 22 Oct 2025, LoRA vs Full Fine-tuning: An Illusion of Equivalence, https://arxiv.org/abs/2410.21228
• Rongguang Ye, Ming Tang, Edith C. H. Ngai, 22 Sep 2025, On-the-Fly Adaptation to Quantization: Configuration-Aware LoRA for Efficient Fine-Tuning of Quantized LLMs, https://arxiv.org/abs/2509.25214
• Yuan Huang, 25 Sep 2025, Fine-tuning of Large Language Models for Domain-Specific Cybersecurity Knowledge, https://arxiv.org/abs/2509.25241
• Hao Ban, Kaiyi Ji, 29 Sep 2025, Rethinking Parameter Sharing for LLM Fine-Tuning with Multiple LoRAs, https://arxiv.org/abs/2509.25414
• Abhinav Rastogi, Adam Yang, Albert Q. Jiang, Alexander H. Liu, Alexandre Sablayrolles, Am\'elie H\'eliou, Am\'elie Martin, Anmol Agarwal, Andy Ehrenberg, Andy Lo, Antoine Roux, Arthur Darcet, Arthur Mensch, Baptiste Bout, Baptiste Rozi\`ere, Baudouin De Monicault, Chris Bamford, Christian Wallenwein, Christophe Renaudin, Cl\'emence Lanfranchi, Cl\'ement Denoix, Corentin Barreau, Darius Dabert Devon Mizelle, Diego de las Casas, Elliot Chane-Sane, Emilien Fugier, Emma Bou Hanna, Gabrielle Berrada, Gauthier Delerce, Gauthier Guinet, Georgii Novikov, Graham Neubig, Guillaume Lample, Guillaume Martin, Himanshu Jaju, Jan Ludziejewski, Jason Rute, Jean-Malo Delignon, JeanHadrien Chabran, Joachim Studnia, Joep Barmentlo, Jonas Amar, Josselin Somerville Roberts, Julien Denize, Karan Saxena, Karmesh Yadav, Kartik Khandelwal, Khyathi Raghavi Chandu, Kush Jain, L\'elio Renard Lavaud, L\'eonard Blier, Lingxiao Zhao, Louis Martin, Lucile Saulnier, Luyu Gao, Marie Pellat, Mathilde Guillaumin, Mathis Felardos, Matthieu Dinot, Maxime Darrin, Maximilian Augustin, Micka\"el Seznec, Neha Gupta, Nikhil Raghuraman, Olivier Duchenne, Patricia Wang, Patrick von Platen, Patryk Saffer, Paul Jacob, Paul Wambergue, Paula Kurylowicz, Philom\`ene Chagniot, Pierre Stock, Pravesh Agrawal, R\'emi Delacourt, Roman Soletskyi, Romain Sauvestre, Sagar Vaze, Sanchit Gandhi, Sandeep Subramanian, Shashwat Dalal, Siddharth Gandhi, Soham Ghosh, Srijan Mishra, Sumukh Aithal, Szymon Antoniak, Teven Le Scao, Thibaut Lavril, Thibault Schueller, Thomas Foubert, Thomas Robert, Thomas Wang, Timoth\'ee Lacroix, Tom Bewley, Valeriia Nemychnikova, Victor Paltz, Virgile Richard, Wen-Ding Li, William Marshall, Xingyao Wang, Xuanyu Zhang, Yihan Wan, Yunhao Tang, 8 Aug 2025, Devstral: Fine-tuning Language Models for Coding Agent Applications, https://arxiv.org/abs/2509.25193
• Darren King, Yaser Atlasi and Gholamreza Rafiee, 28 Sep 2025, DNABERT-2: Fine-Tuning a Genomic Language Model for Colorectal Gene Enhancer Classification, https://arxiv.org/abs/2509.25274
• Chenhua Shi, Gregor Macdonald, Bhavika Jalli, Wanlu Lei, John Zou, Mridul Jain, Joji Philip, 30 Sep 2025, Think Less, Label Better: Multi-Stage Domain-Grounded Synthetic Data Generation for Fine-Tuning Large Language Models in Telecommunications, https://arxiv.org/abs/2509.25736
• Matthew DosSantos DiSorbo, Harang Ju, Sinan Aral, 30 Sep 2025, Teaching AI to Handle Exceptions: Supervised Fine-Tuning with Human-Aligned Judgment, https://arxiv.org/abs/2503.02976
• Jiawei Li, 30 Sep 2025, Detecting Instruction Fine-tuning Attacks on Language Models using Influence Function, https://arxiv.org/abs/2504.09026
• Prashant Govindarajan, Davide Baldelli, Jay Pathak, Quentin Fournier, Sarath Chandar, 30 Sep 2025, CADmium: Fine-Tuning Code Language Models for Text-Driven Sequential CAD Design, https://arxiv.org/abs/2507.09792
• Ruoxing Yang, 6 Oct 2025, DP-Adam-AC: Privacy-preserving Fine-Tuning of Localizable Language Models Using Adam Optimization with Adaptive Clipping, https://arxiv.org/abs/2510.05288
• Yurun Song, Zhuoyi Yang, Ian G. Harris, Sangeetha Abdu Jyothi, 7 Oct 2025, AMAQ: Adaptive Mixed-bit Activation Quantization for Collaborative Parameter Efficient Fine-tuning, https://arxiv.org/abs/2510.05468
• Jiancong Xiao, Bojian Hou, Zhanliang Wang, Ruochen Jin, Qi Long, Weijie J. Su, Li Shen, 16 Oct 2025, Restoring Calibration for Aligned Large Language Models: A Calibration-Aware Fine-Tuning Approach, https://arxiv.org/abs/2505.01997
• Xiaoxue Yang, Bozhidar Stevanoski, Matthieu Meeus, Yves-Alexandre de Montjoye, 16 Oct 2025, Checkpoint-GCG: Auditing and Attacking Fine-Tuning-Based Prompt Injection Defenses, https://arxiv.org/abs/2505.15738

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