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 16

Concrete Subspace Learning based Interference Elimination for Multi-task Model Fusion

arXiv:2312. 06173v2 Announce Type: replace Abstract: Merging models fine-tuned from a common, extensively pre-trained large model but specialized for different tasks has been demonstrated as a cheap and scalable strategy to construct a multi-task model that performs well across diverse tasks.

By Anke Tang, Xianglin Luo, Li Shen, Yong Luo, Liang Ding, Han Hu, Bo Du, Dacheng Tao

fine-tuning

arXiv Machine Learning

Jun 16

Towards a Unified Generative Model for Scarce Time Series with Domain Experts

arXiv:2606. 15172v1 Announce Type: new Abstract: Synthesizing realistic time series with generative models has wide-ranging applications in real-world scenarios.

By Zihao Yao, Qi Zheng, Jiankai Zuo, Yaying Zhang

llms diffusion fine-tuning

arXiv AI

Jun 16

Scaling LLM Reasoning from Minimal Labels: A Semi-Supervised Framework with a Lightweight Verifier

arXiv:2606. 16811v1 Announce Type: new Abstract: For the development of Large language models (LLMs), recent approaches to generating pseudo intermediate reasoning have shown remarkable progress.

By Keizo Kato, Chenhui Chu, Yugo Murawaki, Sado Kurohashi

llms agents nlp fine-tuning benchmarks

arXiv Machine Learning

Jun 16

Your "Pro" LLM Subscription May Actually Be "Free": Exposing Fingerprint Spoofing Risks in LLM Inference Services

arXiv:2606. 16100v1 Announce Type: cross Abstract: As Large Language Model (LLM) APIs become ubiquitous, users increasingly rely on black-box fingerprinting to verify that providers are serving the advertised premium models.

By Jiahao Zhang, Xiuyu Li, Suhang Wang

llms fine-tuning efficiency safety

arXiv Machine Learning

Jun 16

Design and Scheduling of an AI-based Queueing System

arXiv:2406. 06855v3 Announce Type: replace-cross Abstract: To leverage prediction models to make optimal scheduling decisions in service systems, we must understand how predictive errors impact congestion due to externalities on the delay of other jobs.

By Jiung Lee, Hongseok Namkoong, Yibo Zeng

llms fine-tuning safety

arXiv AI

Jun 16

FasterPy: An LLM-based Code Execution Efficiency Optimization Framework

arXiv:2512. 22827v2 Announce Type: replace-cross Abstract: Code often suffers from performance bugs.

By Yue Wu, Minghao Han, Ruiyin Li, Peng Liang, Amjed Tahir, Zengyang Li, Qiong Feng, Mojtaba Shahin

llms rag fine-tuning benchmarks

arXiv Machine Learning

Jun 16

Privacy from Symmetry: Orthogonally Equivariant Transformers for LLM Inference

arXiv:2606. 16461v1 Announce Type: new Abstract: Running large language models locally is often impractical, pushing inference on sensitive text to third-party providers.

By Alexander Yukhimchuk, Andrey Shulga, Mladen Kolar, Martin Tak\'a\v{c}

llms rag fine-tuning safety

arXiv AI

Jun 16

StarOR: Synergizing Tree Search and Test-Time Reinforcement Learning for Optimization Modeling

arXiv:2606. 15197v1 Announce Type: cross Abstract: Optimization modeling is inherently hierarchical, requiring a precise sequence of symbolic commitments.

By Jiajun Li, Yu Ding, Shisi Guan, Ran Hou, Wanyuan Wang

llms reinforcement-learning fine-tuning benchmarks

arXiv AI

Jun 16

Deep Residual Injection for Full-Spectrum Forensic Signal Perception in Multimodal Large Language Models

arXiv:2606. 15880v1 Announce Type: cross Abstract: Multimodal large language models (MLLMs) have been increasingly adopted in forensics for their robust semantic understanding.

By Kaiqing Lin, Zhiyuan Yan, Ruoxin Chen, Ke-Yue Zhang, Yue Zhou, Caiyong Piao, Bin Li, Taiping Yao, Bo Wang, Youchang Xiao, Shouhong Ding

llms fine-tuning multimodal benchmarks

arXiv AI

Jun 16

FusionRS: A Large-Scale RGB-Infrared Remote Sensing Dataset for Dual-Modal Vision-Language Foundation Models

arXiv:2606. 17020v1 Announce Type: cross Abstract: Remote sensing vision-language models have advanced Earth observation understanding, but most existing work remains centered on RGB imagery, leaving the complementary information in infrared data underexplored.

By Jiaju Han, Ben Zhang, Xuemeng Sun, Qike Zhang, Yuxian Dong, Chengyin Hu, Fengyu Zhang, Yiwei Wei, Jiujiang Guo

llms fine-tuning multimodal benchmarks safety

arXiv AI

Jun 16

ACC: Compiling Agent Trajectories for Long-Context Training

arXiv:2605. 21850v2 Announce Type: replace-cross Abstract: Recent development of agents has renewed demand for long-context reasoning capacity of LLMs.

By Qisheng Su, Zhen Fang, Shiting Huang, Yu Zeng, Yiming Zhao, Kou Shi, Ziao Zhang, Lin Chen, Zehui Chen, Lijun Wu, Feng Zhao

llms agents fine-tuning benchmarks

arXiv AI

Jun 16

DYNA : Dynamic Episodic Memory Networks for Augmenting Large Language Models with Temporal Knowledge Graphs in Continuous Learning

arXiv:2606. 15778v1 Announce Type: cross Abstract: Large Language Models (LLMs) struggle to incorporate new knowledge without forgetting or costly retraining.

By Ali Sarabadani, Mahtab Tajvidiyan

llms rag fine-tuning

arXiv AI

Jun 16

PreLort: Prefix-Nested LoRA for Federated Fine-Tuning under Rank Heterogeneity

arXiv:2606. 15963v1 Announce Type: cross Abstract: Federated fine-tuning of large language models using parameter-efficient methods such as LoRA enables privacy-preserving adaptation of foundation models.

By Muhammad Waseem, Nurbek Tastan, Andrej Jovanovic, Nicholas D. Lane, Nils Lukas, Karthik Nandakumar, Samuel Horvath

llms fine-tuning safety

arXiv AI

Jun 16

SDFLoRA: Selective Decoupled Federated LoRA for Privacy-preserving Fine-tuning with Heterogeneous Clients

arXiv:2601. 11219v3 Announce Type: replace-cross Abstract: Federated learning (FL) for large language models (LLMs) has attracted increasing attention as a privacy-preserving approach for adapting models over distributed data, where parameter-efficient methods such as Low-Rank Adaptation (LoRA) are widely adopted to reduce communication and memory costs.

By Zhikang Shen, Jianrong Lu, Haiyuan Wan, Jianhai Chen

llms fine-tuning benchmarks safety

arXiv Machine Learning

Jun 16

Efficient Reinforcement Learning by Guiding World Models with Non-Curated Data

arXiv:2502. 19544v3 Announce Type: replace Abstract: Leveraging offline data is a promising way to improve the sample efficiency of online reinforcement learning (RL).

By Yi Zhao, Aidan Scannell, Wenshuai Zhao, Yuxin Hou, Tianyu Cui, Le Chen, Dieter B\"uchler, Arno Solin, Juho Kannala, Joni Pajarinen

reinforcement-learning robotics fine-tuning

arXiv Machine Learning

Jun 16

CADO: From Imitation to Cost Minimization for Heatmap-based Solvers in Combinatorial Optimization

arXiv:2602. 08210v2 Announce Type: replace Abstract: Heatmap-based solvers have emerged as a promising paradigm for Combinatorial Optimization (CO).

By Hyungseok Song, Deunsol Yoon, Kanghoon Lee, Han-Seul Jeong, Soonyoung Lee, Woohyung Lim

diffusion reinforcement-learning fine-tuning benchmarks safety

arXiv AI

Jun 16

daVinci-kernel: Co-Evolving Skill Selection, Summarization, and Utilization via RL for GPU Kernel Optimization

arXiv:2606. 16497v1 Announce Type: cross Abstract: GPU kernel optimization represents a paradigm where functional correctness is assumed and execution efficiency is the objective.

By Dayuan Fu, Mohan Jiang, Tongyu Wang, Dian Yang, Jiarui Hu, Liming Liu, Jinlong Hou, Pengfei Li

llms rag agents nlp reinforcement-learning fine-tuning

arXiv AI

Jun 16

Where Does Texture Evidence Live in SAM? Features, Proposal Masks, and Texture Segmentation

arXiv:2606. 14755v1 Announce Type: cross Abstract: Texture segmentation stresses foundation segmentation because meaningful regions are defined by material or repeated appearance rather than object identity.

By Nadav Orenstein, Aviad Cohen Zada, Shai Avidan, Gal Oren

computer-vision fine-tuning

arXiv AI

Jun 16

Adaptive and Explicit safe: Triggering Latent Safety Awareness in Large Reasoning Models

arXiv:2606. 16808v1 Announce Type: new Abstract: While Large Reasoning Models (LRMs) excel at complex tasks, they remain highly vulnerable to sophisticated jailbreaks and direct harmful queries.

By Ke Miao, Jiaxin Li, Hongliang Chen, Yuke Hu, Zhan Qin

llms reinforcement-learning fine-tuning multimodal benchmarks safety

arXiv Machine Learning

Jun 16

LiFT: Local Search via Linear Programming for Overfitting-Controlled Transformers

arXiv:2606. 16243v1 Announce Type: new Abstract: This paper proposes a Linear Programming (LP)-based local search framework for fine-tuning pretrained transformer models with explicit control against overfitting.

By Abhishek Shukla, Anikeit Khanna, Ankur Sinha, Faiz Hamid

llms fine-tuning

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