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

Jul 21

DMFNet: Dual-Backbone Multiscale Fusion Network for Urban Scene Classification

arXiv:2607. 16338v1 Announce Type: cross Abstract: This article presents DMFNet, a dual-backbone multiscale feature fusion framework with residual feature propagation and spatial attention for remote sensing scene classification.

By Anamitra Ghosh, Abhiroop Chatterjee, Susmita Ghosh

fine-tuning benchmarks

arXiv Machine Learning

Jul 21

NIRVANA: Structured Pruning Reimagined for Large Language Model Compression

arXiv:2509. 14230v2 Announce Type: replace Abstract: While structured pruning presents a highly effective pathway for accelerating Large Language Model (LLM) inference, existing methods frequently suffer from significant performance degradation and demand computationally retraining to recover capabilities.

By Mengting Ai, Tianxin Wei, Sirui Chen, Jingrui He

llms fine-tuning efficiency benchmarks

arXiv AI

Jul 21

Interactive Task Alignment as a POMDP

arXiv:2607. 16412v1 Announce Type: new Abstract: Current benchmarks for language models primarily evaluate execution on fully specified tasks.

By Andy Dai, Zexue He, Zhenyu Zhang, Alex Pentland, Jiaxin Pei

llms reinforcement-learning fine-tuning benchmarks safety

arXiv AI

Jul 21

GenSyn10: A Multi-Generative AI Dataset For Benchmarking Image Classification

arXiv:2607. 16283v1 Announce Type: cross Abstract: The rapid advancement of generative AI has outpaced our ability to reliably detect its outputs, particularly when detectors encounter generators they have not seen before.

By Md Faraz Kabir Khan, Saeed Anwar, Ghulam Mubashar Hassan

llms diffusion computer-vision fine-tuning multimodal benchmarks

arXiv AI

Jul 21

Rooted Absorbed Prefix Trajectory Balance with Submodular Replay for GFlowNet Training

arXiv:2603. 00454v3 Announce Type: replace-cross Abstract: Generative Flow Networks (GFlowNets) enable fine-tuning large language models to approximate reward-proportional posteriors, but they remain prone to mode collapse, manifesting as prefix collapse and length bias.

By Xi Wang, Wenbo Lu, Shengjie Wang

llms fine-tuning safety

arXiv AI

Jul 21

MADA-RL: Multi-Agent Debate-Aware Reinforcement Learning for Parameter-Efficient Reasoning in Compact Models

arXiv:2607. 18006v1 Announce Type: cross Abstract: Large language models achieve strong reasoning performance, but often at prohibitive training cost - a challenge that is especially acute for compact models ($\leq 4 \, \mathrm{B}$ parameters) trained under limited budgets.

By Martino M. L. Pulici, Cuong Xuan Chu, Evgeny Kharlamov, Zifeng Ding, Volker Tresp, Yunpu Ma

llms agents reinforcement-learning fine-tuning benchmarks

arXiv AI

Jul 21

Phantom Transitions in Language Model Fine-Tuning: A Density-Matrix Analysis

arXiv:2606. 07559v2 Announce Type: replace-cross Abstract: Fine-tuning a language model often fails silently when its correct completion must outrank a near-synonym competitor.

By Vaibhav Prakash, Jayasri Dontabhaktuni

llms rag fine-tuning

arXiv Machine Learning

Jul 21

The Many Senses of Visual Similarity: A Text-Prompted Image Perceptual Metric

arXiv:2607. 18237v1 Announce Type: cross Abstract: Human visual similarity judgments are context-dependent.

By Sheng-Yu Wang, Yotam Nitzan, Aaron Hertzmann, Jun-Yan Zhu, Eli Shechtman, Alexei A. Efros, Richard Zhang

llms fine-tuning multimodal benchmarks

arXiv AI

Jul 21

Environment-free Synthetic Data Generation for API-Calling Agents

arXiv:2607. 16900v1 Announce Type: new Abstract: Training API-calling large language model (LLM) agents demands massive amounts of high-quality trajectories.

By Seanie Lee, Sanjoy Chowdhury, Chao Jiang, Cheng-Yu Hsieh, Ting-Yao Hu, Alexander T Toshev, Oncel Tuzel, Raviteja Vemulapalli

llms agents fine-tuning benchmarks

arXiv AI

Jul 21

A Deep Reinforcement Learning Algorithm for the Vehicle Routing Problem with Stochastic Demands and Outsourcing

arXiv:2607. 16875v1 Announce Type: cross Abstract: We introduce the vehicle routing problem with stochastic demands and outsourcing options (VRP-SDO), in which a logistics service provider partitions customer requests into customers outsourced to a common carrier and customers committed to its fixed fleet.

By Mohsen Dastpak, Fausto Errico, Ola Jabali

reinforcement-learning fine-tuning benchmarks

arXiv Machine Learning

Jul 21

Transferable Low-Rank Convolutional Bases for Onboarding Unseen Medical Imaging Modalities

arXiv:2607. 16888v1 Announce Type: cross Abstract: Deploying a medical imaging model that must later accommodate a modality it has never seen is a recurring practical problem: retraining the shared representation is expensive and destroys performance on the modalities already in service.

By Ranat Das Prangon, Istiaque Ahmed, Shajid Hasan Naim, Waseem Mustak Zisan, Hossain Md Shakhawat

fine-tuning

arXiv AI

Jul 21

A Systematic Evaluation of Trajectory Data Curation for LoRA Fine-Tuning of Code Agents

arXiv:2607. 17205v1 Announce Type: new Abstract: Supervised fine-tuning (SFT) of open-weight LLMs on expert agent trajectories has emerged as a prominent approach to building capable code agents without reliance on proprietary models.

By Yunze Han

llms agents fine-tuning benchmarks

arXiv AI

Jul 21

Geometry-Enhanced Portion Estimation for Multimodal LLMs

arXiv:2607. 16514v1 Announce Type: cross Abstract: Image-based dietary assessment promises to replace costly, bias-prone manual recalls, but portion estimation remains a major blocker.

By Lin Liao, Peng Li

llms fine-tuning multimodal benchmarks safety

arXiv Machine Learning

Jul 21

Lightweight Wrappers for Adapting Time Series Foundation Models to Regional Drought Forecasting

arXiv:2607. 17511v1 Announce Type: new Abstract: Large \emph{Time Series Foundation Models} (TSFMs) demonstrate strong zero-shot forecasting capabilities across diverse domains.

By Wentao Gao, Jiuyong Li, Lin Liu, Thuc Duy Le, Jixue Liu, Yanchang Zhao, Yun Chen

fine-tuning

arXiv AI

Jul 21

Democratizing AI with Small Language Models: Structured Benchmarking and Parameter-Efficient Fine-Tuning for Local Deployment

arXiv:2607. 16202v1 Announce Type: new Abstract: AI democratization is not primarily a question of matching frontier-scale generality; it is a question of whether capable models can be selected, audited, and specialized under hardware and governance constraints that ordinary institutions can actually satisfy.

By Daniel Cersosimo

llms fine-tuning efficiency benchmarks

arXiv AI

Jul 21

Masked Diffusion Language Models are Strong and Steerable Text-Based World Models for Agentic RL

arXiv:2607. 16204v1 Announce Type: new Abstract: Recent growth in reinforcement learning (RL) has surfaced a need for diverse, specialized training environments.

By Darshan Deshpande

llms agents diffusion reinforcement-learning fine-tuning efficiency safety

arXiv AI

Jul 21

JUMP: Single-Pass Membership Inference on Fine-Tuned Diffusion Language Models

arXiv:2607. 16207v1 Announce Type: new Abstract: Membership inference attacks (MIAs) test whether a candidate example appeared in a model's training data.

By Yeachan Jun, Albert No

llms diffusion fine-tuning

arXiv AI

Jul 21

Symbolic Augmentation Closes a Canonical-Equivalence Blind Spot in Neural Fact-Checkers

arXiv:2607. 16212v1 Announce Type: new Abstract: Large language models hallucinate numbers and units when summarizing scientific text, a failure mode that can silently invert a scientific claim.

By Genpei Zhang

llms fine-tuning benchmarks

arXiv Machine Learning

Jul 21

A Framework for Early Sepsis Prediction via Self-Supervised (JEPA) and Federated Representation Learning

arXiv:2607. 16681v1 Announce Type: new Abstract: Early sepsis prediction from electronic health records is challenged by irregular sampling, high missingness, and class imbalance.

By Umair bin Mansoor, Munaf Rashid, Roomi Naqvi

rag fine-tuning efficiency benchmarks

arXiv AI

Jul 21

Shapley Context Pruning: A Cooperative Game Perspective for Context Reranking and Pruning

arXiv:2607. 16209v1 Announce Type: new Abstract: Context reranking and pruning have become essential for improving the efficiency of modern Retrieval-Augmented Generation (RAG) systems, yet an interpretable and unified framework remains underexplored.

By Yanqiao Chen, Dongsheng Hou, Yuhan Rui, Zhen Cao, Yepang Liu

llms rag fine-tuning efficiency benchmarks

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