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

Jul 7

Multimodal Ambivalence/Hesitancy Recognition in Videos for Personalized Digital Health Interventions

arXiv:2604. 11730v4 Announce Type: replace-cross Abstract: Using behavioural science, health interventions focus on behaviour change by providing a framework to help patients acquire and maintain healthy habits that improve medical outcomes.

By Manuela Gonz\'alez-Gonz\'alez, Soufiane Belharbi, Muhammad Osama Zeeshan, Masoumeh Sharafi, Muhammad Haseeb Aslam, Lorenzo Sia, Nicolas Richet, Marco Pedersoli, Alessandro Lameiras Koerich, Simon L Bacon, Eric Granger

llms fine-tuning multimodal

arXiv Machine Learning

Jul 7

Learning Only What Valid Adapters Can Express: Subspace-Constrained Adaptation Against Fine-Tuning Poisoning

arXiv:2607. 05300v1 Announce Type: new Abstract: Parameter-efficient fine-tuning still leaves a broad space of behavior-changing updates reachable, so a poisoned objective can be represented and optimized.

By Fabien Polly

fine-tuning

arXiv AI

Jul 7

RADIO1D: Elastic Representations for Condensed Vision Modeling

arXiv:2607. 03624v1 Announce Type: cross Abstract: This paper challenges the assumption that vision-language models (VLMs) require fixed patch-based 2D vision features.

By Greg Heinrich, Mike Ranzinger, Collin McCarthy, Natan Bagrov, Eugene Khvedchenya, Bryan Catanzaro, Jan Kautz, Andrew Tao, Pavlo Molchanov

llms nlp fine-tuning efficiency multimodal benchmarks safety

arXiv AI

Jul 7

Towards Reliable Local Security Agents: Verifiable Post-Training for Linux Privilege Escalation

arXiv:2603. 17673v2 Announce Type: replace-cross Abstract: LLM agents are becoming increasingly important in the security domain, but leading systems are often closed-source, cloud-based, hard to reproduce or use with sensitive code.

By Philipp Normann, Andreas Happe, J\"urgen Cito, Daniel Arp

llms agents reinforcement-learning fine-tuning benchmarks

arXiv AI

Jul 7

Towards Generalizable Deepfake Image Detection with Vision Transformers

arXiv:2604. 17376v2 Announce Type: replace-cross Abstract: In today's day and age, we face a challenge in detecting deepfake images because of the fast evolution of modern generative models and the poor generalization capability of existing methods.

By Kaliki V Srinanda, M Manvith Prabhu, Hemanth K Mogilipalem, Jayavarapu S Abhinai, Vaibhav Santhosh, Aryan Herur, Deepu Vijayasenan

llms fine-tuning benchmarks

arXiv Machine Learning

Jul 7

The Bayesian Approach to Continual Learning: An Overview

arXiv:2507. 08922v3 Announce Type: replace-cross Abstract: Continual learning is an online paradigm where a learner continually accumulates knowledge from different tasks encountered over sequential time steps.

By Tameem Adel

fine-tuning benchmarks

arXiv Machine Learning

Jul 7

FastCSP: Accelerated Molecular Crystal Structure Prediction with Universal Model for Atoms

arXiv:2508. 02641v2 Announce Type: replace-cross Abstract: Molecular crystal structure prediction (CSP) is essential for applications in pharmaceuticals and organic electronics.

By Vahe Gharakhanyan, Yi Yang, Luis Barroso-Luque, Daniel S. Levine, Sushree Jagriti Sahoo, Brandon M. Wood, Kyle Michel, Muhammed Shuaibi, Gregory J. O. Beran, Viachaslau Bernat, Misko Dzamba, Xiang Fu, Meng Gao, Xingyu Liu, Benjamin K. Miller, Keian Noori, Lafe J. Purvis, Tingling Rao, Ammar Rizvi, Matt Uyttendaele, Andrew J. Ouderkirk, Chiara Daraio, C. Lawrence Zitnick, Arman Boromand, Noa Marom, Zachary W. Ulissi, Anuroop Sriram

fine-tuning efficiency

arXiv AI

Jul 7

InverseCrafter: Efficient Video ReCapture as a Latent Domain Inverse Problem

arXiv:2512. 05672v2 Announce Type: replace-cross Abstract: Recent approaches in controllable novel view video generation often rely on fine-tuning pre-trained Video Diffusion Models (VDMs).

By Yeobin Hong, Suhyeon Lee, Hyungjin Chung, Jong Chul Ye

diffusion fine-tuning

arXiv AI

Jul 7

STELLA: Efficient Sensor-to-LLM Translation for On-Device Human Activity Recognition

arXiv:2607. 03089v1 Announce Type: cross Abstract: HAR is increasingly expected to run continuously on edge devices, yet recent LLM-based methods remain hard to deploy: raw sensor prompts are long, cloud inference adds latency and privacy risk, and fine-tuned LLM pipelines turn general-purpose models into task-specific classifiers.

By Nirhoshan Sivaroopan, Albert Zomaya, Kanchana Thilakarathna

llms rag nlp fine-tuning benchmarks safety

arXiv Machine Learning

Jul 7

Provable Pruning for Efficient 3D Gaussian Splatting via Coresets

arXiv:2607. 02721v1 Announce Type: cross Abstract: 3D Gaussian Splatting (3DGS) enables high-quality real-time novel-view synthesis, but practical scenes often contain millions of Gaussians, making compression essential for deployment on limited hardware.

By Waseem Mousa, Alaa Maalouf

fine-tuning efficiency benchmarks

arXiv AI

Jul 7

TaoSR-AGRL: Adaptive Guided Reinforcement Learning Framework for E-commerce Search Relevance

arXiv:2510. 08048v4 Announce Type: replace-cross Abstract: Query-product relevance prediction is fundamental to e-commerce search and has become even more critical in the era of AI-powered shopping, where semantic understanding and complex reasoning directly shape the user experience and business conversion.

By Jianhui Yang, Yiming Jin, Pengkun Jiao, Chenhe Dong, Zerui Huang, Shaowei Yao, Xiaojiang Zhou, Dan Ou, Haihong Tang

llms reinforcement-learning fine-tuning

arXiv AI

Jul 7

dOPSD: On-Policy Self-Distillation for Diffusion Language Models

arXiv:2607. 04428v1 Announce Type: cross Abstract: Diffusion large language models (dLLMs) generate text by iteratively denoising a masked sequence, offering a parallel alternative to autoregressive models, but eliciting strong reasoning through post-training remains difficult: supervised fine-tuning is off-policy and suffers from exposure bias, while reinforcement learning gives only sparse, sequence-level rewards and is hard to apply without tractable sequence likelihoods.

By Phuong Tuan Dat, Qi Li, Xinchao Wang

llms diffusion reinforcement-learning fine-tuning efficiency safety

arXiv Machine Learning

Jul 7

Context-Constrained Transfer Learning for Tabular Foundation Models via Data Distillation

arXiv:2607. 04809v1 Announce Type: cross Abstract: Tabular Foundation Models (TFMs) have demonstrated strong empirical performance as black-box inference engines through in-context learning.

By Yijun Lin, Sai Li

llms fine-tuning efficiency

arXiv AI

Jul 7

Responsibility Distribution Estimation in Ego-View Accident Videos with Multimodal Large Language Models

arXiv:2607. 03591v1 Announce Type: cross Abstract: Recent studies on multimodal traffic accident understanding have mainly relied on infrastructure-camera footage, satellite imagery, or structured crash records.

By Ryosei Tamura, Andrew Shin

llms agents computer-vision fine-tuning multimodal benchmarks

arXiv AI

Jul 7

ICR-RL: Deep Reinforcement Learning via In-Context Regression

arXiv:2509. 11259v2 Announce Type: replace-cross Abstract: Recent advancements in machine learning have largely been driven by foundation models (FMs) trained on large, diverse datasets, enabling them to generalize effectively to new, related tasks.

By David Schiff, Ofir Lindenbaum, Yonathan Efroni

agents reinforcement-learning fine-tuning benchmarks

arXiv Machine Learning

Jul 7

RL Forgets! Towards Continual Policy Optimization

arXiv:2607. 04364v1 Announce Type: new Abstract: Continual post-training is becoming a central paradigm for adapting vision-language models to evolving tasks.

By Mao-Lin Luo, Zhe-Xu Wang, Zi-Hao Zhou, Bo Ye, Jian Zhao, Min-Ling Zhang, Tong Wei

llms reinforcement-learning fine-tuning multimodal benchmarks

arXiv AI

Jul 7

When Claws Remember but Do Not Tell: Stealthy Memory Injection in Persistent Personal Agents

arXiv:2607. 05189v1 Announce Type: cross Abstract: Persistent personal agents combine long-term memory with access to users' external environments, enabling personalized foreground assistance and proactive background execution.

By Yechao Zhang, Shiqian Zhao, Jiawen Zhang, Jie Zhang, Gelei Deng, Xiaogeng Liu, Chaowei Xiao, Tianwei Zhang

llms agents reinforcement-learning fine-tuning benchmarks

arXiv AI

Jul 7

TORINO: Token Reduction via Interpretable Concept Overlap in Vision-Language Models

arXiv:2607. 04593v1 Announce Type: cross Abstract: Vision-Language Models (VLMs) have demonstrated impressive capabilities across different tasks, but their computational cost is dominated by the large number of visual tokens fed to the language model.

By Riccardo Renzulli, Gabriele Spadaro, Shruthi Gowda, Alaa Eddine Mazouz, Van-Tam Nguyen

llms diffusion fine-tuning efficiency multimodal benchmarks

arXiv Machine Learning

Jul 7

PLoRA: Efficient Concurrent LoRA Training for Large Language Models

arXiv:2508. 02932v2 Announce Type: replace Abstract: Low-Rank Adaptation (LoRA) has gained popularity as a fine-tuning approach for Large Language Models (LLMs) due to its low resource requirements and good performance.

By Minghao Yan, Zhuang Wang, Zhen Jia, Shivaram Venkataraman, Yida Wang

llms fine-tuning efficiency

arXiv AI

Jul 7

CritiqueDriveVLM: From Verifier-Guided Reinforcement Learning to Latent Thought Distillation for Autonomous Driving

arXiv:2607. 04179v1 Announce Type: cross Abstract: End-to-end Vision-Language Models (VLMs) show immense potential in autonomous driving.

By Zhaohong Liu, Hao Ye, Xianlin Zhang, Mengshi Qi

llms agents reinforcement-learning robotics fine-tuning efficiency multimodal benchmarks safety

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