Fine-tuning and adaptation

LoRA, PEFT, instruction tuning and domain adaptation — adapting a pretrained model without paying to train one.

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arXiv Machine Learning

Jun 3

Reading the Finetuning Prior: Verbatim Content Recovery via Contrastive Decoding Diffing

arXiv:2605. 25902v2 Announce Type: replace Abstract: Narrowly finetuned language models memorize implanted content verbatim, but auditing what a deployed model has been taught, without access to its weights or training data, remains an open challenge.

By Micha{\l} Brzozowski, Zuzanna Dubanowska, Enrico Cassano, Neo Christopher Chung

llms fine-tuning benchmarks data-engineering

arXiv Machine Learning

Jun 3

Backdooring Masked Diffusion Language Models

arXiv:2605. 19262v2 Announce Type: replace Abstract: Masked diffusion language models (MDLMs) are emerging as a compelling new paradigm for text generation, but their training-time security remains largely unexplored.

By Daniel Yiming Cao, Chengzhong Wang, Sheng-Yen Chou, Chengyu Huang, Pin-Yu Chen, Shengwei An

llms diffusion fine-tuning

arXiv Machine Learning

Jun 3

DECA: Decentralizing Block-Wise Adam for Efficient LLM Full-Parameter Fine-Tuning on Non-IID Data

arXiv:2606. 03209v1 Announce Type: new Abstract: Fine-tuning large language models (LLMs) in privacy-sensitive and resource-constrained environments remains challenging.

By Yunsheng Yuan, Shaowei Li, Kai Wang, Zhongyuan Sun, Zheng Zhang, Kai Han, Jun Luo, Feng Li

llms fine-tuning safety

arXiv Machine Learning

Jun 3

Self-Soupervision: Cooking Model Soups without Labels

arXiv:2602. 02890v2 Announce Type: replace Abstract: Model soups are strange and strangely effective combinations of parameters.

By Anthony Fuller, James R. Green, Evan Shelhamer

fine-tuning

arXiv Machine Learning

Jun 3

Visual Instruction Tuning Aligns Modalities through Abstraction

arXiv:2606. 03871v1 Announce Type: cross Abstract: Visual instruction tuning effectively adapts a pre-trained Large Language Model (LLM) to process image information alongside text.

By Luis Palacios, Lorenzo Basile, Diego Doimo, Alberto Cazzaniga

llms rag fine-tuning multimodal benchmarks safety

arXiv Machine Learning

Jun 3

Skill-RM: Unifying Heterogeneous Evaluation Criteria via Agent Skill

arXiv:2606. 03980v1 Announce Type: new Abstract: Reward models (RMs) provide critical feedback signals for LLM post-training, notably in reinforced fine-tuning (RFT) and reinforcement learning (RL) pipelines.

By Tao Chen, Gangwei Jiang, Pengyu Cheng, Siyuan Huang, Yihao Liu, Jingwei Ni, Jiaqi Guo, Mengyu Zhou, Kai Tang, Junling Liu, Qinliang Su, Xiaoxi Jiang, Guanjun Jiang

llms agents reinforcement-learning fine-tuning benchmarks

arXiv Machine Learning

Jun 3

Spatial Transcriptomics-Guided Alignment Enhances Molecular Profiling in Pathology Foundation Model

arXiv:2606. 03644v1 Announce Type: new Abstract: Comprehensive molecular profiling is essential for modern precision oncology but remains hindered by prohibitive costs, specimen exhaustion, and protracted turnaround times.

By Fengtao Zhou, Yingxue Xu, Zhengyu Zhang, Yihui Wang, Zhengrui Guo, Ling Liang, Jiabo Ma, Cheng Jin, Ziyi Liu, Huajun Zhou, Hongyi Wang, Du Cai, Chenglong Zhao, Xi Wang, Can Yang, Yu Wang, Wenbin Li, Feng Gao, Zhe Wang, Zhenhui Li, Xiuming Zhang, Li Liang, Hao Chen

fine-tuning multimodal safety

arXiv Machine Learning

Jun 3

Synthetic Hallucinations, Real Gains: Hard Negatives from Frontier Models for FIM Hallucination Mitigation

arXiv:2606. 03130v1 Announce Type: new Abstract: Small open-source code models that power IDE autocomplete still emit hallucinated Fill-in-the-Middle (FIM) completions: syntactically natural calls to methods, parameters, variables, and imports that do not exist in the surrounding project.

By Mahdi Erfanian, Nelson Daniel Troncoso, Aashna Garg, Amabel Gale, Xiaoyu Liu, Pareesa Ameneh Golnari, Shengyu Fu

llms reinforcement-learning fine-tuning benchmarks safety

arXiv Machine Learning

Jun 3

CoughSense: Five-Class Respiratory Disease Classification via Whisper Encoder Fine-Tuning and Dual-Encoder Cross-Attention Fusion with Balanced Contrastive Learning

arXiv:2606. 02998v1 Announce Type: new Abstract: Automated cough analysis offers a path to low-cost respiratory screening, but most existing work stops at binary COVID-19 detection.

By Nikhil Vincent

fine-tuning multimodal benchmarks

arXiv AI

Jun 3

Using Reward Uncertainty to Induce Diverse Behaviour in Reinforcement Learning

arXiv:2606. 03962v1 Announce Type: cross Abstract: Classical reinforcement learning (RL) typically seeks a deterministic policy that maximizes the expected sum of a scalar reward.

By Anthony GX-Chen, Ankit Anand, Gheorghe Comanici, Zaheer Abbas, Eser Ayg\"un, David Smalling, Shibl Mourad, Doina Precup, Andr\'e Barreto, Mark Rowland

llms agents reinforcement-learning fine-tuning

arXiv Machine Learning

Jun 3

RESCAST-100K: A Comprehensive Dataset for Cross-Domain Residential Load and Indoor Temperature Forecasting

arXiv:2606. 02852v1 Announce Type: new Abstract: Accurate short-term forecasting of residential energy load and indoor temperature is essential for home energy management systems, grid-level demand response, and community energy efficiency efforts.

By Jainam Dhruva, Yousaf Raza, A. B. Siddique, Simone Silvestri

llms robotics fine-tuning benchmarks

arXiv Machine Learning

Jun 3

Pruning Deep Neural Networks via the Marchenko--Pastur Distribution

arXiv:2606. 02608v1 Announce Type: new Abstract: We study a Marchenko--Pastur (MP) random-matrix approach to pruning deep neural networks with very small post-pruning fine-tuning budgets.

By Leonid Berlyand, Theo Bourdais, Houman Owhad, Yitzchak Shmalo

fine-tuning efficiency

arXiv AI

Jun 3

Visual Graph Scaffolds for Structural Reasoning in Large Language Models

arXiv:2606. 02673v1 Announce Type: new Abstract: Graphs have been used to enhance large language models (LLMs) for structured reasoning, mostly as external knowledge sources are provided to models at test time.

By Runlin Lei, Xiaokui Xiao, Zhewei Wei

llms nlp fine-tuning efficiency

arXiv AI

Jun 3

Narrow Secret Loyalty Dodges Black-Box Audits

arXiv:2605. 06846v3 Announce Type: replace-cross Abstract: Recent work identifies secret loyalties as a distinct threat from standard backdoors.

By Alfie Lamerton, Fabien Roger

fine-tuning

arXiv AI

Jun 3

X-Restormer++: 1st Place Solution for the UG2+ CVPR 2026 All-Weather Restoration Challenge

arXiv:2605. 13258v2 Announce Type: replace-cross Abstract: In this work, we present our winning solution for the 8th UG2+ Challenge (CVPR 2026) Track 1: Image Restoration under All-weather Conditions.

By Youwei Pan, Leilei Cao, Yingfang Zhu, Fengjie Zhu

fine-tuning

arXiv AI

Jun 3

ProEval: Proactive Failure Discovery and Efficient Performance Estimation for Generative AI Evaluation

arXiv:2604. 23099v2 Announce Type: replace-cross Abstract: Evaluating generative AI models is increasingly resource-intensive due to slow inference, expensive raters, and a rapidly growing landscape of models and benchmarks.

By Yizheng Huang, Wenjun Zeng, Aditi Kumaresan, Zi Wang

fine-tuning benchmarks safety

arXiv AI

Jun 3

Learning the Neighborhood: Contrast-Free Multimodal Self-Supervised Molecular Graph Pretraining

arXiv:2509. 22468v2 Announce Type: replace-cross Abstract: High-quality molecular representations are essential for property prediction and molecular design, yet large labeled datasets remain scarce.

By Boshra Ariguib, Mathias Niepert, Andrei Manolache

llms rag diffusion fine-tuning multimodal benchmarks

arXiv AI

Jun 3

CoMPAS3D: A Dataset and Benchmark for Interactive Motion

arXiv:2507. 19684v2 Announce Type: replace-cross Abstract: Socially interactive humanoid robots must engage with humans through their bodies, adapting in real time to a partner's movement, intent, and abilities.

By Bermet Burkanova, Yasaman Etesam, Payam Jome Yazdian, Trinity Evans, Chuxuan Zhang, Zoe Stanley, Paige Tutt\"os\'i, Angelica Lim

llms robotics fine-tuning multimodal benchmarks safety

arXiv AI

Jun 3

Typhoon: Towards an Effective Task-Specific Masking Strategy for Pre-trained Language Models

arXiv:2303. 15619v2 Announce Type: replace-cross Abstract: The choice of \emph{which} tokens to mask is a central, under-examined design decision in masked language modeling (MLM).

By Muhammed Shahir Abdurrahman, Hashem Elezabi, Bruce Changlong Xu

llms fine-tuning

arXiv Machine Learning

Jun 3

Few-Shot Prediction for Pulsar Noise with Long Short-Term Memory Network

arXiv:2606. 03574v1 Announce Type: cross Abstract: This work proposes a novel solution to predict pulsar timing residuals with limited data, addressing the critical challenge of data scarcity across spin-frequency subgroups of millisecond pulsars in PTA datasets.

By Qingye Tang, Dechao An, Haoran Peng, Yuqi Ouyang

fine-tuning

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