Fine-Tuning

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Fine-tuning starts from a pretrained model and uses task-specific data or feedback to adapt its predictions, representations, or behavior. Approaches range from updating all weights to parameter-efficient methods such as LoRA and adapters, selective layer or neuron updates, and supervised, preference-based, or reinforcement-learning objectives. The techniques are used across language, vision, speech, robotics, and scientific applications, with evaluation spanning task accuracy, domain transfer, safety, and deployment costs. A central challenge is gaining new capabilities without losing useful prior knowledge: outcomes depend on the model, data, and training choices, and improvements on one benchmark may not transfer to other settings.

Latest papers

250 papers23 papers in the last four weeks, down 30% on the four weeks before. 0.1% of all new papers.

Where Does Robustness Live? Neuron-Guided Adaptation for Retrieval-Augmented Language Models
Apr 2, 2026Jae O Lee, Jaemin Kim, Sumyeong Ahn +1Retrieval-Augmented Generation Fine-Tuning
1Hanyang University · 2KENTECH
From SWE-ZERO to SWE-HERO: Execution-free to Execution-based Fine-tuning for Software Engineering Agents
Apr 2, 2026Nikolai Ludwig, Wasi Uddin Ahmad, Somshubra Majumdar +1Software Engineering Agents LLM Agent Training
NVIDIA Santa Clara, CA 95051, USA
Towards Empowering Consumers through Sentence-level Readability Scoring in German ESG Reports
Mar 31, 2026Benjamin Josef Schüßler, Jakob PrangeFine-Tuning
University of Augsburg, Germany · German Center for Addiction Research in Childhood and Adolescence (DZSKJ), University Medical Center Hamburg-Eppendorf, Germany
A Closer Look at Cross-Domain Few-Shot Object Detection: Fine-Tuning Matters and Parallel Decoder Helps
Mar 30, 2026Xuanlong Yu, Youyang Sha, Longfei Liu +2Few-Shot Object Detection Object Detection
Intellindust AI Lab · Suzhou Institute for Advanced Research, USTC
MoE-Sieve: Routing-Guided LoRA for Efficient MoE Fine-Tuning
Mar 25, 2026Andrea ManzoniMixture-of-Experts Fine-Tuning Fine-Tuning
An Adapter-free Fine-tuning Approach for Tuning 3D Foundation Models
Mar 24, 2026Sneha Paul, Zachary Patterson, Nizar BouguilaFine-Tuning 3D Foundation Models
Concordia University, Montreal, Canada
Optimizing Small Language Models for NL2SQL via Chain-of-Thought Fine-Tuning
Mar 24, 2026Anshul Solanki, Sanchit Latawa, Koushik Chakraborty +1Text-to-SQL Fine-Tuning
Google AI, Global Services Delivery
Retrieval-Augmented LLM Agents: Learning to Learn from Experience
Mar 18, 2026Thomas Palmeira Ferraz, Romain Deffayet, Vassilina Nikoulina +2Fine-Tuning LLM Agent Training
NAVER LABS Europe, Meylan, France
How Clued up are LLMs? Evaluating Multi-Step Deductive Reasoning in a Text-Based Game Environment
Mar 17, 2026Rebecca Ansell, Autumn Toney-WailsFine-Tuning Language Models LLM Agent Evaluation
Department of Computer Science, Georgetown University, Washington, DC, USA · Syntheos, Corp; UNU-Merit, Maastricht, NL
Target Concept Tuning Improves Extreme Weather Forecasting
Mar 17, 2026Shijie Ren, Xinyue Gu, Ziheng Peng +6Extreme Weather Forecasting Weather Forecasting
Alibaba Group, Hangzhou, China
An Efficient Heterogeneous Co-Design for Fine-Tuning on a Single GPU
Mar 17, 2026Ruijia Yang, Zeyi WenFine-Tuning Memory-Efficient Fine-Tuning
HKUST(GZ) Guangzhou, China · HKUST Guangzhou, China
Not All Invariants Are Equal: Curating Training Data to Accelerate Program Verification with SLMs
Mar 16, 2026Ido Pinto, Yizhak Yisrael Elboher, Haoze Wu +2Fine-Tuning Training Data Curation
Hebrew University of Jerusalem, Israel · Amherst Col · VMware Research by Broadcom, USA.
SciDesignBench: Benchmarking and Improving Language Models for Scientific Inverse Design
Mar 13, 2026David van Dijk, Ivan VrkicInverse Design Large Language Model-Guided Optimization
CellType Inc.
Simple Recipe Works: Vision-Language-Action Models are Natural Continual Learners with Reinforcement Learning
Mar 12, 2026Jiaheng Hu, Jay Shim, Chen Tang +4Continual Learning for VLA Models VLA Model Adaptation
UT Austin · UCLA · NTU +1
Representation Finetuning for Continual Learning
Mar 11, 2026Haihua Luo, Xuming Ran, Tommi Kärkkäinen +4Continual Learning Fine-Tuning
Faculty of Information Technology, University of Jyväskylä, Jyväskylä, Finland · National University of Singapore, Singapore, Singapore · School of Computer Science and Technology, Dalian University of Technology, Dalian, China
Reflection-Driven Self-Improving Language Model Agents for Adaptive RAN Slicing
Mar 11, 2026Yuanhao Li, Haozhe Wang, Geyong Min +2Self-Improving Agents Language Model-Based Control
Department of Computer Science, Faculty of Environment, Science and Economy, University of Exeter, Exeter, EX4 4QF, UK. · British Telecom, UK.
Improving the accuracy of physics-informed neural networks via last-layer retraining
Mar 4, 2026Saad Qadeer, Panos StinisNeural PDE Solvers Physics-Informed ML
Advanced Computing, Mathematics and Data Division, Pacific Northwest National Laboratory, Richland, WA
From Language to Action in Arabic: Reliable Structured Tool Calling via Data-Centric Fine-Tuning
Mar 4, 2026Omer Nacar, Deema Alquffari, Saleh Alsharideh +22Arabic Language Models LLM Fine-Tuning
Tuwaiq Academy, Riyadh, Saudi Arabia · Wakeb Company · Tahakom +3
$π$ -StepNFT: Wider Space Needs Finer Steps in Online RL for Flow-based VLAs
Mar 2, 2026Siting Wang, Xiaofeng Wang, Zheng Zhu +7Flow-Based VLA Models Vision-Language-Action Models
GigaAI · Institute of Automation, Chinese Academy of Sciences · University of Chinese Academy of Sciences +4
Learn Hard Problems During RL with Reference Guided Fine-tuning
Mar 1, 2026Yangzhen Wu, Shanda Li, Zixin Wen +5LLM Mathematical Reasoning RL for Language Model Reasoning
UC Berkeley · Carnegie Mellon University · ByteDance Seed
Enhancing Safety of Large Language Models via Embedding Space Separation
Mar 1, 2026Xu Zhao, Xiting Wang, Weiran ShenLLM Safety LLM Fine-Tuning
Gaoling School of Artificial Intelligence, Renmin University of China.
MC-Search: Evaluating and Enhancing Multimodal Agentic Search with Structured Long Reasoning Chains
Mar 1, 2026Xuying Ning, Dongqi Fu, Tianxin Wei +7Multimodal RAG Multimodal Search Agents
University of Illinois Urbana-Champaign · Meta · IBM Research +1
Benchmarking Few-shot Transferability of Pre-trained Models with Improved Evaluation Protocols
Feb 28, 2026Xu Luo, Ji Zhang, Lianli Gao +2Few-Shot Learning Transfer Learning
University of Electronic Science and Technology of China, Chengdu, China. · Southwest Jiaotong University, Chengdu, China. · Tongji University, Shanghai, China.
Transcoder Adapters for Reasoning-Model Diffing
Feb 24, 2026Nathan Hu, Jake Ward, Thomas Icard +1LLM Interpretability Large Reasoning Models
Stanford University · MATS
A Theory of How Pretraining Shapes Inductive Bias in Fine-Tuning
Feb 23, 2026Nicolas Anguita, Francesco Locatello, Andrew M. Saxe +4Neural Network Generalization Representation Learning
Department of Engineering, University of Cambridge · Institute of Science and Technology, Austria (ISTA) · Gatsby Computational Neuroscience Unit and Sainsbury Wellcome Centre, UCL +1
Efficient Tail-Aware Generative Optimization via Flow Model Fine-Tuning
Feb 18, 2026Zifan Wang, Riccardo De Santi, Xiaoyu Mo +3Molecule Design Fine-Tuning
KTH Royal Institute of Technology · ETH Zurich · ETH AI Center +2
Silent Inconsistency in Data-Parallel Full Fine-Tuning: Diagnosing Worker-Level Optimization Misalignment
Feb 16, 2026Hong Li, Zhen Zhou, Honggang Zhang +4Distributed Training Fine-Tuning
School of Transportation, Southeast University, Nanjing, Jiangsu 211189 · Department of Logistics and Maritime Studies, The Hong Kong Polytechnic University, Kowloon, Hong Kong, China 999077
Selective Fine-Tuning for Targeted and Robust Concept Unlearning
Feb 8, 2026Mansi, Avinash Kori, Francesca Toni +1Fine-Tuning Adversarial Robustness
Department of Computing Imperial College London, United Kingdom
Vulnerability of LLMs' Stated Beliefs? LLMs Belief Resistance Check Through Strategic Persuasive Conversation Interventions
Jan 20, 2026Fan Huang, Haewoon Kwak, Jisun AnLLM Evaluation Fine-Tuning
Indiana University Bloomington, United States
DR-LoRA: Dynamic Rank LoRA for Fine-Tuning Mixture-of-Experts Models
Jan 8, 2026Guanzhi Deng, Bo Li, Ronghao Chen +5Mixture-of-Experts Language Models Fine-Tuning
City University of Hong Kong · Tsinghua University · Peking University +2

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Where Does Robustness Live? Neuron-Guided Adaptation for Retrieval-Augmented Language Models

Jae O Lee, Jaemin Kim, Sumyeong Ahn, Seo Yeon Park

1Hanyang University · 2KENTECH

Retrieval-Augmented Generation Fine-Tuning Knowledge Augmentation for Language Models LLM Fine-Tuning Neural Network Robustness Retrieval Robustness+2

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Retrieval-Augmented Language Models (RALMs) have shown strong potential in knowledge-intensive tasks, yet they remain vulnerable when retrieved contexts are noisy or irrelevant. Robustness against such contexts requires two distinct capabilities: abstention when contexts are uninformative, and selective extraction when relevant evidence is buried in noise. Yet existing methods face two key limitations: they do not train separately for these two capabilities, and they adapt the model at a coarse layer- or module-level granularity, overlooking that only a small subset of neurons is strongly activated for a given input. We propose NeuRIT, a Neuron-guided Robust Instruction-Tuning framework built on a localization-first perspective. NeuRIT mines context-aware neurons associated with relevant and irrelevant context processing, and uses them as anchors to selectively adapt both the identified neuron groups and the layers in which they concentrate. NeuRIT then performs two-stage instruction tuning that teaches complementary behaviors: suppress generation when there is nothing to extract, and extract relevant evidence when there is. NeuRIT consistently outperforms strong baselines across diverse QA benchmarks and generator backbones. Our code is available at https://github.com/HYU-ARK-Lab/NeuRIT.