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 AI

Jun 11

When Probing Accuracy Saturates, Fragility Resolves: A Complementary Metric for LLM Pre-Training Analysis

arXiv:2606. 11375v1 Announce Type: cross Abstract: Standard linear probing declares a property "encoded" when a classifier on hidden states achieves high accuracy.

By Orion Reblitz-Richardson

llms fine-tuning

arXiv AI

Jun 11

Self-Prompting Small Language Models for Privacy-Sensitive Clinical Information Extraction

arXiv:2605. 04221v2 Announce Type: replace-cross Abstract: Clinical named entity recognition from dental progress notes is challenging because documentation is highly unstructured, domain-specific, and often privacy-sensitive.

By Yao-Shun Chuang, Tushti Mody, Uday Pratap Singh, Shirindokht Shiraz, Chun-Teh Lee, Ryan Brandon, Muhammad F Walji, Xiaoqian Jiang, Bunmi Tokede

llms nlp reinforcement-learning fine-tuning benchmarks safety

arXiv AI

Jun 11

Towards Data-free and Training-free Compression for Speech Foundation Models Using Parameter Clustering

arXiv:2606. 11836v1 Announce Type: cross Abstract: This paper presents a novel data-free and training-free compression approach for speech foundation models using channelwise clustering via k-means.

By Haoning Xu, Zhaoqing Li, Huimeng Wang, Youjun Chen, Chengxi Deng, Mengzhe Geng, Xunying Liu

fine-tuning efficiency multimodal

arXiv AI

Jun 11

Architecture-Aware Reinforcement Learning Makes Sliding-Window Attention Competitive in Math Reasoning

arXiv:2606. 11634v1 Announce Type: new Abstract: The rapid progress of reasoning and agentic large language models (LLMs) has increased the demand for long-context inference, but self-attention (SA) scales quadratically with context length.

By Kai Liu, Peijie Dong, Xinchen Xie, Jianfei Gao, Qipeng Guo, Xiaowen Chu, Shaoting Zhang, Kai Chen

llms agents reinforcement-learning fine-tuning benchmarks

arXiv Machine Learning

Jun 11

Compatibility-Aware Dynamic Fine-Tuning for Large Language Models

arXiv:2606. 11206v1 Announce Type: cross Abstract: Supervised Fine-Tuning (SFT) is the predominant paradigm for aligning large language models (LLMs), yet it suffers from optimization instability and limited generalization.

By Yucheng Zhou, Junwei Sheng, Qianning Wang, Jianbing Shen

llms reinforcement-learning fine-tuning

arXiv AI

Jun 11

Tabular Foundation Models for Clinical Survival Analysis via Survival-Aware Adaptation

arXiv:2606. 12006v1 Announce Type: cross Abstract: Predicting time-to-event outcomes such as mortality is a fundamental task in clinical decision-making, commonly addressed through survival analysis.

By Minh-Khoi Pham, Luca Cotugno, Alina Sirbu, Tai Tan Mai, Martin Crane, Marija Bezbradica

fine-tuning benchmarks

arXiv AI

Jun 11

Does the Question Really Matter? Training-Free Data Selection for Vision-Language SFT

arXiv:2603. 09715v2 Announce Type: replace Abstract: Visual instruction tuning is crucial for improving vision-language large models (VLLMs).

By Peng Sun, Yi Yang, Huawen Shen, Yi Ban, Tianfan Fu, Yanbo Wang, Yuqiang Li

fine-tuning multimodal

arXiv Machine Learning

Jun 11

MobileFineTuner: A Mobile-Native Framework for On-Device LLM Fine-Tuning in Real-World Embedded AI Applications

arXiv:2512. 08211v2 Announce Type: replace Abstract: Large language models (LLMs) are moving from cloud-centric services toward on-device embedded AI, where models interact with private, longitudinal signals sensed from users and their physical environments.

By Jiaxiang Geng, Lunyu Zhao, Yiyi Lu, Bing Luo

llms agents fine-tuning

arXiv AI

Jun 11

The Art of Interrogation: Consistency Amplifies Factuality in Spatial Reasoning

arXiv:2606. 11918v1 Announce Type: new Abstract: Current Large Reasoning Models (LRMs) exhibit remarkable general capabilities but significantly underperform in spatial reasoning tasks.

By Theo Uscidda, Marta Tintore Gazulla, Maks Ovsjanikov, Federico Tombari, Leonidas Guibas

reinforcement-learning fine-tuning safety

arXiv AI

Jun 11

Harness In-Context Operator Learning with Chain of Operators

arXiv:2606. 12318v1 Announce Type: cross Abstract: Neural operators approximate mappings between function spaces, but often generalize poorly to other operators and usually require fine-tuning or retraining.

By Minghui Yang, Ling Guo, Liu Yang

llms fine-tuning

arXiv Machine Learning

Jun 11

OGPO: Sample Efficient Full-Finetuning of Generative Control Policies

arXiv:2605. 03065v2 Announce Type: replace Abstract: Generative control policies (GCPs), such as diffusion- and flow-based control policies, have emerged as effective parameterizations for robot learning.

By Sarvesh Patil, Mitsuhiko Nakamoto, Manan Agarwal, Shashwat Saxena, Jesse Zhang, Giri Anantharaman, Cleah Winston, Chaoyi Pan, Douglas Chen, Nai-Chieh Huang, Zeynep Temel, Oliver Kroemer, Sergey Levine, Abhishek Gupta, Hongkai Dai, Paarth Shah, Max Simchowitz

diffusion reinforcement-learning robotics fine-tuning benchmarks

arXiv AI

Jun 11

Models That Know How Evaluations Are Designed Score Safer

arXiv:2605. 28591v2 Announce Type: replace-cross Abstract: The validity of AI safety evaluations depends on models behaving consistently across controlled and deployment settings.

By Katharina Deckenbach, Haritz Puerto, Jonas Geiping, Sahar Abdelnabi

fine-tuning benchmarks safety

arXiv AI

Jun 11

Learning to Inject: Automated Prompt Injection via Reinforcement Learning

arXiv:2602. 05746v2 Announce Type: replace-cross Abstract: Prompt injection is a critical vulnerability in LLM agents, yet the strongest methods still rely on human red-teamers and hand-crafted prompts.

By Xin Chen, Jie Zhang, Florian Tram\`er

llms agents reinforcement-learning fine-tuning safety

arXiv AI

Jun 11

Weakly Supervised Segmentation as Semantic-Based Regularization

arXiv:2605. 13674v2 Announce Type: replace-cross Abstract: Weakly supervised semantic segmentation (WSSS) trains dense pixel-level segmentation models from partial or coarse annotations such as bounding boxes, scribbles, or image-level tags.

By Stefano Colamonaco, Andrei-Bogdan Florea, Jaron Maene

computer-vision fine-tuning benchmarks

arXiv Machine Learning

Jun 11

AsFT: Anchoring Safety During LLM Fine-Tuning Within Narrow Safety Basin

arXiv:2506. 08473v4 Announce Type: replace Abstract: Fine-tuning large language models (LLMs) improves performance but introduces critical safety vulnerabilities: even minimal harmful data can severely compromise safety measures.

By Shuo Yang, Qihui Zhang, Yuyang Liu, Xiaojun Jia, Kunpeng Ning, Jiayu Yao, Jigang Wang, Hailiang Dai, Yibing Song, Li Yuan

llms fine-tuning safety

arXiv AI

Jun 11

MLaGA: Multimodal Large Language and Graph Assistant

arXiv:2506. 02568v2 Announce Type: replace Abstract: Large Language Models (LLMs) have demonstrated substantial efficacy in advancing graph-structured data analysis.

By Dongzhe Fan, Yi Fang, Jiajin Liu, Djellel Difallah, Qiaoyu Tan

llms fine-tuning multimodal

arXiv AI

Jun 11

ProcessThinker: Enhancing Multi-modal Large Language Models Reasoning via Rollout-based Process Reward

arXiv:2606. 11209v1 Announce Type: cross Abstract: Visual question answering increasingly requires multi-step reasoning.

By Jingpei Wu, Xiao Han, Weixiang Shen, Boer Zhang, Zifeng Ding, Volker Tresp

llms nlp reinforcement-learning fine-tuning multimodal benchmarks

arXiv AI

Jun 11

Embodied-R1.5: Evolving Physical Intelligence via Embodied Foundation Models

arXiv:2606. 11324v1 Announce Type: cross Abstract: We introduce Embodied-R1.

By Yifu Yuan, Yaoting Huang, Xianze Yao, Yutong Li, Shuoheng Zhang, Linqi Han, Pengyi Li, Jiangeng Sun, Wenting Jia, Zhao Zhang, Yuhao Liu, Ruihao Liao, Yucheng Hu, Qiyu Wu, Yuxiao Li, Zibin Dong, Fei Ni, Yan Zheng, Shuyang Gu, Yi Ma, Hongyao Tang, Han Hu, Jianye Hao

llms reinforcement-learning robotics fine-tuning multimodal benchmarks

arXiv AI

Jun 11

When Roleplaying, Do Models Believe What They Say?

arXiv:2606. 11502v1 Announce Type: cross Abstract: Language models can state that "the Earth orbits the Sun" and, when role-playing Aristotle, assert the opposite.

By Benjamin Sturgeon, David Africa, Sid Black

llms fine-tuning

arXiv Machine Learning

Jun 11

Parameter-Efficient Adapter Tuning for Tabular-Image Multimodal Learning

arXiv:2606. 11682v1 Announce Type: cross Abstract: Tabular-image multimodal learning aims to improve predictive modeling by jointly using structured tabular attributes and visual data.

By Jiaqi Luo

rag fine-tuning multimodal benchmarks

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