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 30

LoRA-Tuned Large Language Models for Dementia Detection via Multi-View Speech-Derived Features

arXiv:2606. 28445v1 Announce Type: cross Abstract: Early detection of dementia enables timely intervention, and reflecting cognitive impairment, spontaneous speech offers a non-invasive screening modality.

By Jonghyeon Park, Olivier Jiyoun Jung, Myungwoo Oh

llms fine-tuning multimodal benchmarks

arXiv AI

Jun 30

DuoMem: Towards Capable On-Device Memory Agents via Dual-Space Distillation

arXiv:2606. 29961v1 Announce Type: cross Abstract: Large Language Model (LLM)-based agents can solve complex procedural tasks by interacting with environments over multiple turns, but this ability typically depends on large models, long contexts, and repeated inference calls.

By Peyman Hosseini, Ondrej Bohdal, Ahmed Alajrami, Andrea Maracani, Ignacio Castro, Matthew Purver, Mete Ozay, Savas Ozkan, Taha Ceritli

llms agents robotics fine-tuning efficiency benchmarks

arXiv AI

Jun 30

Data-Efficient Multimodal Alignment for Histopathology-based Molecular Prediction

arXiv:2606. 29949v1 Announce Type: cross Abstract: H&E-stained whole-slide images offer cohort-scale availability and rich spatial context but lack molecular specificity, whereas bulk RNA-seq provides transcriptome-wide resolution at high cost with limited archival availability.

By Dominik Winter, Dominik Vonficht, Lo\"ic Le Bescond, Christian Gebbe, Marco Rosati, Richard J. Chen, Markus Schick, Ross Stewart, Nicolas Brieu

llms fine-tuning multimodal safety

arXiv AI

Jun 30

SEATauBench: Adapting Tool-Agent-User Evaluation Into Low-Resource Southeast Asian Languages

arXiv:2606. 28715v1 Announce Type: cross Abstract: While AI development and evaluation for Southeast Asia (SEA) has grown rapidly, agent capabilities in regional languages are still poorly understood despite its importance to sovereign AI.

By My Chiffon Nguyen, Aulia Adila, Saksorn Ruangtanusak, Kittiphat Leesombatwathana, Vissuta Gunawan Lim, Patomporn Payoungkhamdee, Samuel Cahyawijaya

agents fine-tuning benchmarks

arXiv AI

Jun 30

Child-Centric Voice Anonymization in Single and Multi-Speaker Speech via Domain-Adapted SSL Models

arXiv:2606. 29897v1 Announce Type: cross Abstract: Voice anonymization aims to protect speaker identity while preserving linguistic content and speech usability.

By Pranav Tushar, Xiao Xiao Miao, Rong Tong

fine-tuning safety

arXiv AI

Jun 30

Do We Still Need Fine Tuning? Turkish Sentiment Analysis in the Era of Large Language Model

arXiv:2606. 29614v1 Announce Type: cross Abstract: This study examines whether supervised fine-tuning remains necessary for Turkish sentiment analysis in the era of large language models.

By Sercan Karaka\c{s}, Yusuf \c{S}im\c{s}ek

llms nlp fine-tuning

arXiv AI

Jun 30

Learning from Mistakes: Rollout-Retrieval Lifelong Policy Learning for Autonomous Driving

arXiv:2606. 30537v1 Announce Type: cross Abstract: Autonomous driving policies should be able to improve continually as deployment exposes them to increasingly diverse and long-tail traffic situations.

By Cheng Gong, Haoyang Wang, Chao Lu, Zirui Li, Jianwei Gong

agents robotics fine-tuning benchmarks

arXiv AI

Jun 30

Beyond Scaling Law: A Data-Efficient Distillation Framework for Reasoning

arXiv:2508. 09883v2 Announce Type: replace-cross Abstract: Large language models (LLMs) demonstrate remarkable reasoning capabilities in tasks such as algorithmic coding and mathematical problem-solving.

By Xiaojun Wu, Xiaoguang Jiang, Huiyang Li, Jucai Zhai, Dengfeng Liu, Qiaobo Hao, Huang Liu, Zhiguo Yang, Ji Xie, Ninglun Gu, Jin Yang, Kailai Zhang, Yelun Bao, Jun Wang

llms reinforcement-learning fine-tuning efficiency benchmarks

arXiv Machine Learning

Jun 30

BaRA: Bayesian Adaptive Rank Allocation for Parameter-Efficient Fine-Tuning

arXiv:2606. 29184v1 Announce Type: new Abstract: While Low-rank adaptation (LoRA) enables highly efficient fine-tuning by constraining task-specific updates to fixed low-rank subspaces, this rigid design limits representational flexibility and often results in overconfident predictions and miscalibrated uncertainty, especially in low-data regimes.

By Zhibin Duan, Yuhong Wang, Jiahong Fu, Zongsheng Yue, Bo Chen, Zongben Xu

fine-tuning benchmarks

arXiv AI

Jun 30

GPC: Large-Scale Generative Pretraining for Transferable Motor Control

arXiv:2606. 29148v1 Announce Type: cross Abstract: Developing controllers capable of completing a wide range of tasks in a natural and life-like manner is a key challenge in enabling practical applications of physics-based character animation.

By Yi Shi, Yifeng Jiang, Chen Tessler, Xue Bin Peng

llms nlp reinforcement-learning fine-tuning efficiency

arXiv AI

Jun 30

Reward-Free Code Alignment from Pretrained or Fine-Tuned LLM: Unpacking the Trade-offs for Code Generation

arXiv:2606. 28998v1 Announce Type: cross Abstract: Large Language Model (LLM) alignment trains an LLM using preference data to produce outputs that better meet established quality standards.

By Gias Uddin, Sanjeepan Sivapiran

llms reinforcement-learning fine-tuning benchmarks safety

arXiv AI

Jun 30

Customized Generative AI Agent for Transportation Engineering Practice: A Development and Continued Pre-training Guideline

arXiv:2606. 29014v1 Announce Type: new Abstract: Recent advancements in generative artificial intelligence (AI) and large language models (LLMs) have shown significant promise in automating complex reasoning, summarization, and question-answering tasks.

By Dianwei Chen (Terry), Yuan-Zheng Lei (Terry), Zifan Zhang (Terry), Yuchen Liu (Terry), Xianfeng (Terry), Yang

llms agents nlp fine-tuning benchmarks safety

arXiv Machine Learning

Jun 30

Your Data Manifold is Secretly a Reward Model: Shell-LCC for Text-to-Video Generation

arXiv:2606. 30248v1 Announce Type: cross Abstract: Recent text-to-video (T2V) diffusion models rely heavily on auxiliary reward signals (e.

By Shihao Zhang, Yuguang Yan, Junzhe Zhang, Wei Zhao, Bohan Wang, Hanwang Zhang

diffusion reinforcement-learning fine-tuning

arXiv AI

Jun 30

McMg: A Learned Phase-Space Multi-channel Multigrid Preconditioner for Helmholtz Equation

arXiv:2606. 30495v1 Announce Type: cross Abstract: Solving heterogeneous Helmholtz equations at high wavenumbers remains challenging because the discretized operator is indefinite, pollution degrades phase accuracy, and scalar coarse-grid correction can discard the local phase and propagation-direction information carried by oscillatory errors.

By Jiwei Jia, Xinliang Liu, Juntao Wang, Jinchao Xu

fine-tuning

arXiv AI

Jun 30

Translating Natural Language to Strategic Temporal Specifications via LLMs

arXiv:2606. 30441v1 Announce Type: cross Abstract: A rigorous formalization of system requirements is a fundamental prerequisite for the verification of Multi-Agent Systems (MAS).

By Marco Aruta, Francesco Improta, Vadim Malvone, Aniello Murano, Vladana Perlic

llms agents fine-tuning

arXiv AI

Jun 30

The Heterogeneous Safety Impacts of Benign Multilingual Fine-Tuning

arXiv:2606. 28843v1 Announce Type: cross Abstract: Fine-tuning a large language model is a ubiquitous method for enhancing its capability on a specific downstream task.

By Will Hawkins, Kaivalya Rawal, Jonathan Rystr{\o}m, Stratis Tsirtsis, Zihao Fu, Greta Warren, Ryan Brown, Eoin Delaney, Sandra Wachter, Brent Mittelstadt, Chris Russell

llms fine-tuning safety

arXiv AI

Jun 30

Dockerless: Environment-Free Program Verifier for Coding Agents

arXiv:2606. 28436v1 Announce Type: cross Abstract: Program verifiers play a central role in training coding agents, including selecting trajectories for supervised fine-tuning (SFT) and providing rewards for reinforcement learning (RL).

By Wenhao Zeng, Yuling Shi, Xiaodong Gu, Chao Hu, Chaofan Wang, Yuhao Cui, Hongting Zhou, Mengnan Qi, Jianqiao Wangni, Zhaojian Yu, Shuzheng Gao, Kai Cai, Shilin He

agents reinforcement-learning fine-tuning benchmarks

arXiv AI

Jun 30

TRACE: Temporal Relationship-Aware Conversational Entrainment Detection in Dyadic Speech

arXiv:2606. 30543v1 Announce Type: cross Abstract: With the proliferation of speech AI agents, understanding emotional entrainment in conversational interaction has become increasingly important.

By Sathvik Manikantan Napa Ugandhar, Hao Zhang, Alison Gunzler, Yuzhe Wang, Thomas Thebaud, Georgi Tinchev, Venkatesh Ravichandran, Laureano Moro-Vel\'azquez

rag agents fine-tuning multimodal

arXiv AI

Jun 30

Skin-R1: Clinical Knowledge-Guided Dermatological Diagnosis Using Vision-Language Models

arXiv:2511. 14900v2 Announce Type: replace-cross Abstract: Vision--language models (VLMs) have recently shown promise for assisting clinical reasoning in dermatological diagnosis.

By Zehao Liu, Weijieying Ren, Jipeng Zhang, Tianxiang Zhao, Jingxi Zhu, Xiaoting Li, Vasant G Honavar

llms reinforcement-learning fine-tuning multimodal benchmarks

arXiv Machine Learning

Jun 30

Optimizer Memory Makes Shuffle Order a First-Order Source of Fine-Tuning Noise

arXiv:2606. 29554v1 Announce Type: new Abstract: Shuffle order can be a larger source of fine-tuning noise than a memoryless analysis predicts: fixed-clock optimizer memory makes local equal-multiset contrasts first order in the learning rate rather than second order, and the resulting order channel can be large enough for a single seed to flip a close A/B comparison.

By John Sweeney

fine-tuning

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