LLM Fine-Tuning

LLM: Large Language Model

LLM fine-tuning studies how additional training can adapt a pretrained model to new tasks, specialized knowledge, languages, or desired behaviors. Common approaches include supervised fine-tuning on curated or synthetic examples, preference optimization and reinforcement learning, and parameter-efficient methods such as LoRA and QLoRA that update only a small part of the model. Researchers also develop data-selection methods and selective layer or neuron updates to reduce training cost, and evaluate results on settings such as reasoning, code generation, multilingual tasks, and domain-specific question answering. A central challenge is gaining reliable task performance without losing general abilities or safety; results depend on the data, model, and evaluation, and retrieval can sometimes be a better alternative for adding knowledge.

Latest papers

758 papers76 papers in the last four weeks, up 15% on the four weeks before. 0.4% of all new papers.

AutoQRA: Joint Optimization of Mixed-Precision Quantization and Low-rank Adapters for Efficient LLM Fine-Tuning
Feb 25, 2026Changhai Zhou, Shiyang Zhang, Yuhua Zhou +5LLM Fine-Tuning Quantization-Aware LoRA
Fudan University · Yale University · Zhejiang University +1
Qwen-BIM: developing large language model for BIM-based design with domain-specific benchmark and dataset
Feb 24, 2026Jia-Rui Lin, Yun-Hong Cai, Xiang-Rui Ni +2Benchmark Design LLM Evaluation
Department of Civil Engineering, Tsinghua University, Beijing, China, 100084 · Key Laboratory of Digital Construction and Digital Twin, Ministry of Housing and Urban-Rural Development, Beijing, China, 100084 · PetroChina Planning & Engineering Institute, Beijing, China, 100083
ID-LoRA: Efficient Low-Rank Adaptation Inspired by Matrix Interpolative Decomposition
Feb 24, 2026Xindian Ma, Rundong Kong, Peng Zhang +2LLM Fine-Tuning Low-Rank Adaptation
Tianjin University, Tianjin, China
Rethinking LoRA for Privacy-Preserving Federated Learning in Large Models
Feb 23, 2026Jin Liu, Yinbin Miao, Ning Xi +1Federated Fine-Tuning Low-Rank Adaptation
School of Cyber Engineering, Xidian University · College of Intelligence and Computing, Tianjin University
Anatomy of Unlearning: The Dual Impact of Fact Salience and Model Fine-Tuning
Feb 23, 2026Anna Borisiuk, Andrey Savchenko, Alexander Panchenko +1Machine Unlearning LLM Unlearning
AIRI · Skoltech · Sber AI Lab +2
Astra: Activation-Space Tail-Eigenvector Low-Rank Adaptation of Large Language Models
Feb 22, 2026Kainan Liu, Yong Zhang, Ning Cheng +4LLM Fine-Tuning Low-Rank Adaptation
Ping An Technology (Shenzhen) Co., Ltd., China
SeedFlood: A Step Toward Scalable Decentralized Fine-Tuning of LLMs
Feb 20, 2026Jihun Kim, Dongyeop Lee, Namhoon LeeCommunication-Efficient Distributed Training Zeroth-Order Optimization
POSTECH, pohang, Republic of Korea.
Influence-Preserving Proxies for Gradient-Based Data Selection in LLM Fine-tuning
Feb 19, 2026Sirui Chen, Yunzhe Qi, Mengting Ai +4Training Data Selection LLM Fine-Tuning
University of Illinois Urbana-Champaign
Small LLMs for Medical NLP: a Systematic Analysis of Few-Shot, Constraint Decoding, Fine-Tuning and Continual Pre-Training in Italian
Feb 19, 2026Pietro Ferrazzi, Mattia Franzin, Alberto Lavelli +1Small Language Models Clinical NLP
Fondazione Bruno Kessler, Povo, Trento, Italy · University of Padova, Padova, Italy
Entropy-Based Data Selection for Language Models
Feb 19, 2026Hongming Li, Yang Liu, Chao HuangData Selection for Language Model Fine-Tuning Training Data Selection
School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing 100083, China · National Key Laboratory of General Artificial Intelligence, Beijing Institute for General Artificial Intelligence, Beijing 100086, China · Shunde Innovation School, University of Science and Technology Beijing, Foshan 528399, China
Improving LLM-based Recommendation with Self-Hard Negatives from Intermediate Layers
Feb 19, 2026Bingqian Li, Bowen Zheng, Xiaolei Wang +5Large Language Model-Based Recommendation LLM Fine-Tuning
GSAI, Renmin University of China Beijing, China · Meituan Beijing, China
Powering Up Zeroth-Order Training via Subspace Gradient Orthogonalization
Feb 19, 2026Yicheng Lang, Changsheng Wang, Yihua Zhang +4Zeroth-Order Optimization Memory-Efficient Fine-Tuning
Michigan State University · University of Minnesota · University of California, Santa Barbara +2
FLoRG: Federated Fine-tuning with Low-rank Gram Matrices and Procrustes Alignment
Feb 19, 2026Chuiyang Meng, Ming Tang, Vincent W. S. WongLLM Fine-Tuning Federated Fine-Tuning
The University of British Columbia · Southern University of Science and Technology
Claim Automation using Large Language Model
Feb 18, 2026Zhengda Mo, Zhiyu Quan, Eli O'Donohue +1LLM Fine-Tuning Business Process Automation
Actuarial and Risk Management Sciences, University of Illinois at Urbana-Champaign · PCMI Corporation
NeST: Neuron Selective Tuning for LLM Safety
Feb 18, 2026Sasha Behrouzi, Lichao Wu, Mohamadreza Rostami +1LLM Safety Alignment Safety-Preserving Fine-Tuning of Language Models
Technical University of Darmstadt · University of Bristol
Learning to Learn from Language Feedback with Social Meta-Learning
Feb 18, 2026Jonathan Cook, Diego Antognini, Martin Klissarov +2Meta-Learning LLM Fine-Tuning
Google DeepMind
The Geometry of Alignment Collapse: When Fine-Tuning Breaks Safety
Feb 17, 2026Max Springer, Chung Peng Lee, Blossom Metevier +5LLM Safety Alignment LLM Alignment
Princeton University
How to Train Your Long-Context Visual Document Model
Feb 16, 2026Austin VeselkaVisual Question Answering Vision-Language Models
LightOn
RT-SFT: Text Style Transfer from Non-Parallel Corpora by Roundtrip Translation
Feb 16, 2026Ruoxi Liu, Philipp KoehnText Style Transfer Text Generation
Unlocking Reasoning Capability on Machine Translation in Large Language Models
Feb 16, 2026Sara Rajaee, Sebastian Vincent, Alexandre Berard +3Structured Reasoning in Language Models Large Reasoning Models
University of Amsterdam · Cohere · Cohere Labs
D2-LoRA: A Synergistic Approach to Differential and Directional Low-Rank Adaptation
Feb 16, 2026Nozomu Fujisawa, Masaaki KondoLLM Fine-Tuning Low-Rank Adaptation
Keio University
A Critical Look at Targeted Instruction Selection: Disentangling What Matters (and What Doesn't)
Feb 16, 2026Nihal V. Nayak, Paula Rodriguez-Diaz, Neha Hulkund +2Instruction-Tuning Data Selection Training Data Selection
Harvard University · MIT · Kempner Institute
Explainable Token-level Noise Filtering for LLM Fine-tuning Datasets
Feb 16, 2026Yuchen Yang, Wenze Lin, Enhao Huang +6LLM Fine-Tuning Training Data Curation
1The State Key Laboratory of Blockchain and Data Security, Zhejiang University · 2Hangzhou HighTech Zone (Binjiang) Blockchain and Data Security Research Institute, China · 3Qiuzhen College, Tsinghua University +2
Query as Anchor: Scenario-Adaptive User Representation via Large Language Model
Feb 16, 2026Jiahao Yuan, Yike Xu, Jinyong Wen +9User Modeling Representation Learning
Ant Group · East China Normal University · Zhejiang University
The Appeal and Reality of Recycling LoRAs with Adaptive Merging
Feb 12, 2026Haokun Liu, Gyung Hyun Je, Marco Ciccone +3LLM Fine-Tuning Language Model Merging
University of Toronto, Canada · Vector Institute, Toronto, Canada · Mistral AI (work done before joining) +2
Patch the Distribution Mismatch: RL Rewriting Agent for Stable Off-Policy SFT
Feb 11, 2026Jiacheng Wang, Zhijie Liu, Ping Jian +4Supervised Fine-Tuning Reinforcement Learning
Beijing Institute of Technology, Beijing, China · Tsinghua University, Beijing, China
Rethinking Bayesian Optimization for Co-Optimizing LLM Training Configurations
Feb 9, 2026Zhiliang Chen, Alfred Wei Lun Leong, Shao Yong Ong +6Bayesian Optimization LLM Fine-Tuning
Department of Computer Science, National University of Singapore, Singapore · Agency for Science, Technology and Research (A*STAR), Singapore
The Role of Dataset Linguistic Structure in the Cultural Awareness of Large Language Models
Feb 1, 2026Reem I. Masoud, Chen Feng, Shunta Asano +3LLM Fine-Tuning Cultural Alignment
University College London · King Abdulaziz University · Queen’s University Belfast +3
Distilling Token-Trained Models into Byte-Level Models
Feb 1, 2026Zishuo Bao, Jiaqi Leng, Junxiong Wang +2LLM Fine-Tuning Byte-Level Language Model
Fuzhou University · NYU Shanghai · Fudan University +2
Bayesian-LoRA: Probabilistic Low-Rank Adaptation of Large Language Models
Jan 28, 2026Moule Lin, Shuhao Guan, Andrea Patane +2Bayesian Neural Networks Confidence Estimation in Language Models
School of Computer Science and Statistics, Trinity College Dublin, Dublin, Ireland and Lero the Research Ireland Centre for Software, Ireland · School of Computer Science, University College Dublin, Dublin, Ireland

Feb 25, 2026J/K move · Enter open · S save

AutoQRA: Joint Optimization of Mixed-Precision Quantization and Low-rank Adapters for Efficient LLM Fine-Tuning

Changhai Zhou, Shiyang Zhang, Yuhua Zhou, Qian Qiao+4

Fudan University · Yale University · Zhejiang University+1

LLM Fine-Tuning Quantization-Aware LoRA Memory-Efficient Fine-Tuning Mixed-Precision Quantization

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Quantization followed by parameter-efficient fine-tuning has emerged as a promising paradigm for downstream adaptation under tight GPU memory constraints. However, this sequential pipeline fails to leverage the intricate interaction between quantization bit-width and LoRA rank. Specifically, a carefully optimized quantization allocation with low quantization error does not always translate to strong fine-tuning performance, and different bit-width and rank configurations can lead to significantly varying outcomes under the same memory budget. To address this limitation, we propose AutoQRA, a joint optimization framework that simultaneously optimizes the bit-width and LoRA rank configuration for each layer during the mixed quantized fine-tuning process. To tackle the challenges posed by the large discrete search space and the high evaluation cost associated with frequent fine-tuning iterations, AutoQRA decomposes the optimization process into two stages. First, it first conducts a global multi-fidelity evolutionary search, where the initial population is warm-started by injecting layer-wise importance priors. This stage employs specific operators and a performance model to efficiently screen candidate configurations. Second, trust-region Bayesian optimization is applied to locally refine promising regions of the search space and identify optimal configurations under the given memory budget. This approach enables active compensation for quantization noise in specific layers during training. Experiments show that AutoQRA achieves performance close to full-precision fine-tuning with a memory footprint comparable to uniform 4-bit methods.