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

Sep 1

Hybrid Offline-Online Multi-Agent Decision Transformers for Wireless Resource Management

arXiv:2608.28878v1 Announce Type: cross Abstract: This paper develops a hybrid offline-online multi-agent reinforcement learning framework based on decision transformers. The policy is first pretrain...

By Yiming Zhang, Kun Yang, Cong Shen, Dongning Guo

llms agents reinforcement-learning fine-tuning

arXiv AI

Sep 1

The Differential Reasoning Router: Operationalizing Cost-Aware LLM Annotation in E-commerce

arXiv:2608.30224v1 Announce Type: cross Abstract: Large Language Models (LLMs) are increasingly used to annotate structured product data in e-commerce, but early deployment often begins as a cold-sta...

By Cheng Lyu, Jingyue Zhang, Vinny DeGenova, Mengwei Li, Yuanli Pei

llms fine-tuning

arXiv AI

Sep 1

Beyond Token-Level Guidance: Inference-Time Alignment of Specialized LLMs via Cross-Family Representation Steering

arXiv:2608.30319v1 Announce Type: cross Abstract: Large language models (LLMs) finetuned for specialized domains represent crucial high-impact applications. Inference-time alignment improves safety d...

By Jin Gan, Xin Li, Jun Luo

llms fine-tuning benchmarks safety

arXiv AI

Sep 1

SemPOI-RL: Aligning LLM Semantic Reasoning for Interpretable Out-of-Town POI Sequential Generation

arXiv:2608.30399v1 Announce Type: cross Abstract: Large language models (LLMs) exhibit strong semantic reasoning and open-ended generation abilities, but aligning these abilities with structured sequ...

By Yunqi Liu, Yang Zhang, Ruixing Zhang, Liangzhe Han, Yi Qiao, Tongyu Zhu, Leilei Sun

llms reinforcement-learning fine-tuning safety

arXiv AI

Sep 1

Enhancing Low-Resource Language Reasoning via High-Resource Language Feature Transfer

arXiv:2608.30462v1 Announce Type: cross Abstract: Large language models exhibit substantial performance variation across languages, even when solving semantically equivalent tasks. Existing analyses...

By Minju Song, Hyeon Hwang, Junhyun Lee, Jaewoo Kang

llms nlp fine-tuning benchmarks

arXiv AI

Sep 1

Reading the News: Adapting Large Language Models to Swedish Journalism Through Continued Pre-Training

arXiv:2608.30609v1 Announce Type: cross Abstract: Large language models are increasingly capable in general, but their utility can remain modest in niche or understudied areas. One approach to addres...

By Lukas Borggren, Jenny Kunz, Marco Kuhlmann

llms fine-tuning benchmarks

arXiv AI

Sep 1

RailSyn: Diagnosis-Guided Image Generation for Traceable Data Completion in Railway Foreign Object Detection

arXiv:2608.30709v1 Announce Type: cross Abstract: Railway foreign object detection (RFOD) is critical to safe railway operation, yet scarce real positive samples incompletely represent task-relevant...

By Quan Hao, Chenxi Zhang, Ziyang Tao, Yuyuan Zhou, Yudong Wang, Rui Shi, Lechuan Xu, Changhao Liu, Liguo Zhang

agents diffusion computer-vision fine-tuning

arXiv AI

Sep 1

Learning from What You Retrieve: Online RL Fine-Tuning for Semantic Retrieval

arXiv:2608.30753v1 Announce Type: cross Abstract: In large-scale e-commerce retrieval, dual-encoder retrievers are op- timized for contrastive similarity, whereas downstream rerankers capture finer-g...

By Shaowei Wei, Chong Huang, Songtao Fang, Jin Zhang, Zhuojun Wang, Chengfu Huo

rag reinforcement-learning fine-tuning efficiency benchmarks

arXiv AI

Sep 1

MusGU+: Toward a Musician-Centered Evaluation Framework and Discovery Tool for Generative Music AI

arXiv:2608.30940v1 Announce Type: cross Abstract: Generative music systems are increasingly presented as tools that democratize music creation, yet their practical suitability for musicians remains u...

By Laura Ib\'a\~nez-Mart\'inez, Roser Batlle-Roca, Xavier Serra, Mart\'in Rocamora

fine-tuning

arXiv Computation and Language

Sep 1

Unlocking Fine-Grained Translation Quality Estimation in LRMs through Mutually Boosting Implicit and Explicit Reasoning

arXiv:2605.31378v2 Announce Type: replace Abstract: Large Reasoning Models (LRMs) still struggle with fine-grained translation quality estimation (QE), even with long reasoning chains. We argue that...

By Renfei Dang, Xinye Wang, Zhejian Lai, Weilu Xu, Shimin Tao, Daimeng Wei, Min Zhang, Shujian Huang

reinforcement-learning fine-tuning

arXiv AI

Sep 1

Rubric-to-Code Credit Assignment for Reinforcement Learning

arXiv:2608.27906v2 Announce Type: replace Abstract: Interactive web application generation requires models to produce usable HTML, CSS, and JavaScript applications from natural language requests. Unl...

By Rui Jin, Jikai Chen, Yihan Chen, Hao Zhou, Demin Zhu, Kaichen Yang, Dong Wang, Linjian Mo, Chenyi Zhuang

llms reinforcement-learning fine-tuning benchmarks

arXiv AI

Sep 1

An Efficient Sparse Fine-Tuning with Low Quantization Error via Neural Network Pruning

arXiv:2502.11439v3 Announce Type: replace-cross Abstract: Fine-tuning is an important step in adapting foundation models such as large language models to downstream tasks. To make this step more acce...

By Cen-Jhih Li, Aditya Bhaskara

llms fine-tuning efficiency benchmarks

arXiv Computation and Language

Sep 1

A Simple Method to Enhance Pre-trained Language Models with Speech Tokens for Classification

arXiv:2512.07571v3 Announce Type: replace Abstract: This paper presents a simple method that allows to easily enhance textual pre-trained large language models with speech information, when fine-tune...

By Nicolas Calbucura, Jose Guillen, Valentin Barriere

llms rag nlp fine-tuning multimodal

arXiv Computation and Language

Sep 1

Make an Offer They Can't Refuse: Grounding Bayesian Persuasion in Real-World Dialogues without Pre-Commitment

arXiv:2510.13387v3 Announce Type: replace Abstract: Large language models (LLMs) still struggle with strategic persuasion, largely because existing approaches either neglect information asymmetry or...

By Buwei He, Yang Liu, Zhaowei Zhang, Zixia Jia, Yang Yu, Huijia Wu, Zhaofeng He, Zilong Zheng, Yipeng Kang

llms fine-tuning

arXiv AI

Sep 1

SimCRAFT: Distilling Remote Sensing Agents via Synthetic Trajectories and Contextual Retrieval-Augmented Fine-Tuning

SimCRAFT is a model‑agnostic framework that distills remote sensing orchestration into a compact 7B‑scale model. It creates a large, constraint‑validated workflow planning corpus (SimRS‑14k) using a multi‑agent synthesis engine and a Mock Execution Engine, then fine‑tunes the model with Contextual Retrieval‑Augmented Fine‑Tuning (CRAFT) to reason analogically. Experiments show SimCRAFT‑7B outperforms open‑weight LLMs and rivals advanced closed‑source models, providing a lightweight, efficient baseline for autonomous remote sensing deployment.

By Haoran Wang, Jing Yao, Xu Yang, Zeqing Wang, Yang Zhang, Pedram Ghamisi, Zhengchao Chen

llms rag agents fine-tuning

arXiv AI

Sep 1

Robustness of Vision Language Models Against Split-Image Harmful Input Attacks

arXiv:2602.08136v2 Announce Type: replace-cross Abstract: Vision-Language Models (VLMs) are now a core part of modern AI. Recent work proposed several visual jailbreak attacks using single/ holistic...

By Md Rafi Ur Rashid, MD Sadik Hossain Shanto, Vishnu Asutosh Dasu, Shagufta Mehnaz

llms reinforcement-learning fine-tuning efficiency multimodal benchmarks safety

arXiv Computation and Language

Sep 1

DataFoundry: Evolving Data Preparators via Recursive Self-Improvement

arXiv:2608.29966v1 Announce Type: new Abstract: Domain adaptation of large language models increasingly depends on constructing high-quality training data, yet existing data-preparation pipelines typ...

By Cehao Yang, Xiaojun Wu, Xueyuan Lin, Chengjin Xu, Xuhui Jiang, Hui Xiong, Jian Guo

llms fine-tuning

arXiv AI

Sep 1

SketchVLM: Vision language models can annotate images to explain thoughts and guide users

arXiv:2604.22875v3 Announce Type: replace-cross Abstract: When answering questions about images, humans naturally point, label, and draw to explain their reasoning. In contrast, modern vision-languag...

By Brandon Collins, Logan Bolton, Hung Huy Nguyen, Mohammad Reza Taesiri, Trung Bui, Anh Totti Nguyen

llms fine-tuning multimodal benchmarks

arXiv AI

Sep 1

ActiveAugment: Online Active Learning for Augmentation Selection in Deep Learning

arXiv:2608.28923v1 Announce Type: cross Abstract: Data augmentation is a cornerstone of deep learning pipelines, yet existing strategies treat it as a static, model-agnostic preprocessing step, eithe...

By Noah Videcrantz, Mostafa Mehdipour Ghazi

llms fine-tuning benchmarks

arXiv AI

Sep 1

Where Does Robustness Live? Neuron-Guided Adaptation for Retrieval-Augmented Language Models

arXiv:2604.02194v2 Announce Type: replace-cross Abstract: Retrieval-Augmented Language Models (RALMs) have shown strong potential in knowledge-intensive tasks, yet they remain vulnerable when retriev...

By Jae O Lee, Jaemin Kim, Sumyeong Ahn, Seo Yeon Park

llms rag fine-tuning benchmarks

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