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 25

NeuroShield: A Device-Agnostic Foundation Model for EEG Authentication

arXiv:2606. 20673v2 Announce Type: replace Abstract: A central challenge in EEG authentication is that models are typically tied to the acquisition settings in which they are trained.

By Matin Fallahi, Patricia Arias-Cabarcos, Thorsten Strufe

llms rag fine-tuning

arXiv Machine Learning

Jun 25

TL++: Accuracy and Privacy Preserving Traversal Learning for Distributed Intelligent Systems

arXiv:2606. 25627v1 Announce Type: new Abstract: Distributed intelligent systems increasingly need to train across data silos without centralizing raw data.

By Erdenebileg Batbaatar, Young Yoon

fine-tuning safety

arXiv Machine Learning

Jun 25

EPTS: Elastic Post-Training Sparsity for Efficient Large Language Model Compression

arXiv:2606. 25285v1 Announce Type: new Abstract: Post-Training Sparsity (PTS) has emerged as a crucial paradigm for compressing Large Language Models to facilitate efficient deployment on resource-constrained devices.

By Ke Xu, Jiaqi Wan, Wenhao Hu, Han Pu, Xiaoyun Wang

llms fine-tuning efficiency benchmarks

arXiv Machine Learning

Jun 25

Memory-Efficient Policy Libraries with Low-Rank Adaptation in Reinforcement Learning

arXiv:2606. 25700v1 Announce Type: new Abstract: When fine-tuning Large Language Models (LLMs), there has been success in minimizing both memory usage and computation with Parameter-Efficient Fine-Tuning (PEFT), like Low Rank Adaptation (LoRA).

By Samuel Valland Lyngset, Tor Viljen Raanaas, Gard Sveipe, Eirik M{\o}ller Nilsen, Jim Torresen, Kai Olav Ellefsen, Tobias L{\o}mo

llms reinforcement-learning robotics fine-tuning

arXiv Machine Learning

Jun 25

Curvature-Guided Mixing for MLLM Adaptation

arXiv:2606. 24963v1 Announce Type: cross Abstract: Fine-tuning Multimodal Large Language Models (MLLMs) on specialized tasks often leads to catastrophic forgetting of their general capabilities.

By Jinglong Yang, Jiaxuan He, Wenjian Huang, Zhan Zhuang, Jianguo Zhang

llms fine-tuning multimodal

arXiv Machine Learning

Jun 25

Why Multi-Step Tool-Use Reinforcement Learning Collapses and How Supervisory Signals Fix It

arXiv:2606. 26027v1 Announce Type: cross Abstract: Tool use enables large language models (LLMs) to perform complex tasks, and recent agentic reinforcement learning (RL) methods show promise for enhancing model capabilities.

By Yupu Hao, Zhuoran Jin, Huanxuan Liao, Kang Liu, Jun Zhao

llms agents reinforcement-learning fine-tuning

arXiv Machine Learning

Jun 25

The Hitchhiker's Guide to Agentic AI: From Foundations to Systems

arXiv:2606. 24937v1 Announce Type: cross Abstract: The Hitchhiker's Guide to Agentic AI is a comprehensive practitioner's reference for building autonomous AI systems.

By Haggai Roitman

llms rag agents reinforcement-learning fine-tuning efficiency safety

arXiv Machine Learning

Jun 25

From Uncertain to Safe: Conformal Adaptation of Diffusion Models for Safe PDE Control

arXiv:2502. 02205v4 Announce Type: replace Abstract: The application of deep learning for partial differential equation (PDE)-constrained control is gaining increasing attention.

By Peiyan Hu, Xiaowei Qian, Wenhao Deng, Rui Wang, Haodong Feng, Ruiqi Feng, Tao Zhang, Long Wei, Yue Wang, Zhi-Ming Ma, Tailin Wu

diffusion fine-tuning safety

arXiv Machine Learning

Jun 25

Geometry-Anchored Transport Framework for Exemplar-Free Class-Incremental Learning

arXiv:2606. 25347v1 Announce Type: new Abstract: Exemplar-free class-incremental learning (EFCIL) requires stable decision boundaries within a shifting feature space.

By Hongye Xu, Bartosz Krawczyk

fine-tuning safety

arXiv Machine Learning

Jun 25

The Geometry of Sequential Learning: Lie-Bracket Prediction of Transfer Order

arXiv:2606. 24993v1 Announce Type: new Abstract: Sequential learning is order-dependent: from Pile-style next-token domain adaptation to instruction-SFT and DPO, N candidate sources induce N!

By John Sweeney

llms diffusion reinforcement-learning fine-tuning benchmarks

arXiv Machine Learning

Jun 25

Supervised Reinforcement Learning for the Coordination of Distributed Energy Resources

arXiv:2606. 24947v1 Announce Type: new Abstract: The increasing integration of distributed energy resources (DERs) is crucial for power system decarbonization, yet unlocking DERs' flexibility is challenged by their inherent uncertainties and modelling complexity.

By Haoyuan Deng, Yihong Zhou, Thomas Morstyn, Yi Wang

llms reinforcement-learning fine-tuning benchmarks

arXiv Machine Learning

Jun 25

Weight Space Representation Learning via Neural Field Adaptation

arXiv:2512. 01759v3 Announce Type: replace Abstract: We investigate the potential of weights to serve as effective representations, focusing on neural fields.

By Zhuoqian Yang, Mathieu Salzmann, Sabine S\"usstrunk

diffusion fine-tuning

arXiv Machine Learning

Jun 25

Improved Large Language Diffusion Models

arXiv:2606. 25331v1 Announce Type: cross Abstract: Modern large language models are predominantly trained with autoregressive factorization and causal attention.

By Shen Nie, Qiyang Min, Shaoxuan Xu, Zihao Huang, Yuxuan Song, Yong Shan, Yankai Lin, Wayne Xin Zhao, Chongxuan Li, Ji-Rong Wen

llms diffusion fine-tuning benchmarks

arXiv Machine Learning

Jun 25

Digital Twin-Driven Adaptive Sim-to-Real Alignment via Reinforcement Learning for Vibration-Based Bearing Health Monitoring Under Data Scarcity

arXiv:2606. 24954v1 Announce Type: new Abstract: Vibration-based health monitoring of rotating machinery requires reliable fault diagnosis under operational data constraints, yet condition assessment remains challenged by structural scarcity of fault events and heterogeneous sim-to-real gaps in digital twin-generated signals.

By Jinghan Wang, Yanjun Chen, Wei Zhang, Wentao Wu, Tianchen Liu, Gaoliang Peng

reinforcement-learning robotics fine-tuning safety

arXiv Machine Learning

Jun 25

WinDOM: Self-Family Distillation for Small-Model GUI Grounding

arXiv:2606. 25964v1 Announce Type: cross Abstract: Small ($\sim$2B) GUI-grounding agents are attractive for on-device deployment, accessibility tooling, and low-cost iteration, but at this scale they face two open recipe questions: how to obtain bounding-box training data without expensive human annotation, and how to combine supervised fine-tuning with reinforcement learning.

By Chengheng Li-Chen, Zhiqian Zhou, Hao Chen, Nicolas Chauvin

agents computer-vision reinforcement-learning fine-tuning efficiency

arXiv Machine Learning

Jun 25

SyncLoop: A Multimodal Dual-Loop Framework for Self-Improving Mathematical Reasoning

arXiv:2507. 16518v3 Announce Type: replace-cross Abstract: Recent advances in multimodal large language models (MLLMs) have shown impressive reasoning capabilities.

By Xiuwei Chen, Wentao Hu, Hanhui Li, Yongxin Wang Jun Zhou, Zisheng Chen, Meng Cao, Yihan Zeng, Kui Zhang, Yu-Jie Yuan, Jianhua Han, Hang Xu, Xiaodan Liang

llms reinforcement-learning fine-tuning multimodal benchmarks

arXiv Machine Learning

Jun 25

The Generalization Spectrum: A Chromatographic Approach to Evaluating Learning Algorithms

arXiv:2606. 25450v1 Announce Type: new Abstract: Traditional evaluations measure a learning algorithm's final performance on an i.

By Jinghan Zhang, Zerui Cheng, Shiqi Chen, Ge Zhang, Wenhao Huang, Jiashuo Liu, Junxian He, Tianle Cai

reinforcement-learning fine-tuning efficiency benchmarks

arXiv Machine Learning

Jun 25

MacroLens: A Multi-Task Benchmark for Contextual Financial Reasoning under Macroeconomic Scenarios

arXiv:2606. 24950v1 Announce Type: new Abstract: Financial decision-making is contextual: forecasting prices, valuing companies, and assessing event exposure weigh price history, accounting fundamentals, macroeconomic regime, and contemporaneous text.

By Patara Trirat, Jin Myung Kwak, Jay Heo, Heejun Lee, Sung Ju Hwang

llms fine-tuning benchmarks

arXiv Machine Learning

Jun 25

Distribution Preference Optimization: A Fine-grained Perspective for LLM Unlearning

arXiv:2510. 04773v2 Announce Type: replace Abstract: As Large Language Models (LLMs) demonstrate remarkable capabilities learned from vast corpora, concerns regarding data privacy and safety are receiving increasing attention.

By Kai Qin, Jiaqi Wu, Jianxiang He, Haoyuan Sun, Yifei Zhao, Xu Wang, Bin Liang, Yongzhe Chang, Cheng Li, Tiantian Zhang, Houde Liu

llms fine-tuning benchmarks safety

Hugging Face Trending Papers

Jun 25

Multipath Adaptive Gated Bottleneck Latent ODE with Raman Data Fusion for Cell Culture Process Forecasting

Mammalian cell-culture processes underpin the manufacture of many biopharmaceuticals, yet keeping a run on track is hard: critical process parameters drift over days, and an off-specification trend is often confirmed too late to intervene. Early-stage, multi-day forecasts could enable timely adjustment of feeding, sampling, and control, but bioprocess forecasting is challenging because measurements are sparse and irregularly sampled, operating conditions are heterogeneous across cell lines and media, and runs with near-identical early behaviour can diverge into different futures.

fine-tuning

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