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 2

GUI-Perturbed: Domain Randomization Reveals Systematic Brittleness in GUI Grounding Models

arXiv:2604. 14262v2 Announce Type: replace-cross Abstract: GUI grounding models report over 85% accuracy on standard benchmarks, yet drop 27-56 percentage points when instructions require spatial reasoning rather than direct element naming.

By Yangyue Wang, Harshvardhan Sikka, Yash Mathur, Tony Zhou, Jinu Nyachhyon, Pranav Guruprasad

fine-tuning benchmarks

arXiv AI

Jul 2

Mechanical Conscience: A Mathematical Framework for Dependability of Machine Intelligence

arXiv:2605. 03847v3 Announce Type: replace Abstract: Distributed collaborative intelligence (DCI), encompassing edge-to-edge architectures, federated learning, transfer learning, and swarm systems, creates environments in which emergent risk is structurally unavoidable: locally correct decisions by individual agents compose into globally unacceptable behavioral trajectories under uncertainty.

By Munkhdelgerekh Batzorig, Purevbaatar Ganbold, Kyungbin Park, Pilkong Jeong, Kangbin Yim

agents reinforcement-learning fine-tuning

arXiv Machine Learning

Jul 2

FRAME: Learning the Adaptation Domain with a Mixture of Fractional-Fourier Experts

arXiv:2607. 00162v1 Announce Type: new Abstract: Parameter-efficient fine-tuning (PEFT) reparameterizes weight updates in a fixed basis: low-rank adapters operate in the spatial domain, while a recent line of spectral methods operates in a fixed Fourier domain.

By Tom Saliencro, Maya Lindqvist, Rohan Desai, Priya Nair, Daniel Whitmore

llms fine-tuning benchmarks

arXiv AI

Jul 1

A Three-Phase Foundation Model for Tax-Aware Personalized Portfolio Management

arXiv:2606. 30997v1 Announce Type: new Abstract: We present a three-phase deep reinforcement learning system for personalized portfolio management that addresses three limitations shared by all prior financial RL work: 1) ticker lock-in, 2) monolithic objectives , and 3) static user models.

By Ramin Pishehvar

reinforcement-learning fine-tuning

arXiv Machine Learning

Jul 1

Nonlinearity-Aware LoRA: Structured Gate Adaptation under Low-Rank Constraints

arXiv:2606. 31717v1 Announce Type: new Abstract: Low-rank adaptation (LoRA) is commonly viewed as an update-space approximation to full fine-tuning, yet this view is incomplete for self-gated Transformer feed-forward networks.

By Shuai Yuan, Sudong Cai, Bingzhi Chen, Shuyuan Zheng, Chuan Xiao, Makoto Onizuka, Rui Mao

llms fine-tuning multimodal benchmarks

arXiv Machine Learning

Jul 1

Probing Memorization of Tabular In-Context Learning

arXiv:2606. 31208v1 Announce Type: new Abstract: Large tabular models (LTMs), i.

By Francesco Capano, Jonas B\"ohler

llms fine-tuning benchmarks

arXiv AI

Jul 1

Toxicity Assessment in Preclinical Histopathology via Class-Aware Mahalanobis Distance for Known and Novel Anomalies

arXiv:2602. 02124v2 Announce Type: replace-cross Abstract: Drug-induced toxicity is a leading cause of preclinical and early-clinical failure, making early detection critical.

By Olga Graf, Dhrupal Patel, Peter Gro{\ss}, Charlotte Lempp, Matthias Hein, Fabian Heinemann

llms computer-vision fine-tuning safety

arXiv AI

Jul 1

Generalizing Numerical Reasoning in Table Data through Operation Sketches and Self-Supervised Learning

arXiv:2604. 21495v2 Announce Type: replace-cross Abstract: Numerical reasoning over expert-domain tables often exhibits high in-domain accuracy but limited robustness to domain shift.

By Hanjun Cho, Gahyun Yoo, Hanseong Kim, Jay-Yoon Lee

llms fine-tuning

arXiv AI

Jul 1

Dataset Construction for Training LLM to Learn Analog Circuit Knowledge

arXiv:2508. 10409v3 Announce Type: replace-cross Abstract: This paper constructs a textual dataset for training large language models (LLMs) to learn analog circuit knowledge and customizes LLM training techniques.

By Zihao Chen, Ji Zhuang, Jinyi Shen, Xiaoyue Ke, Xinyi Yang, Mingjie Zhou, Zhuoyao Du, Xu Yan, Zhouyang Wu, Zhenyu Xu, Jiangli Huang, Li Shang, Xuan Zeng, Fan Yang

llms agents fine-tuning benchmarks

arXiv AI

Jul 1

Z-1: Efficient Reinforcement Learning for Vision-Language-Action Models

arXiv:2606. 31846v1 Announce Type: cross Abstract: Vision-Language-Action (VLA) models offer a promising framework for robotic manipulation by connecting language instructions, visual observations, and continuous control.

By Lang Cao, Renhong Chen, Luyi Li, Peng Wang, Mofan Peng, Yitong Li

reinforcement-learning robotics fine-tuning multimodal benchmarks

arXiv AI

Jul 1

Beyond Compilation: Evaluating Faithful Natural-Language-to-Lean Statement Formalization

arXiv:2606. 31002v1 Announce Type: new Abstract: Theorem-proving benchmarks evaluate proof search against fixed formal statements, but natural-language-to-Lean formalization must generate the formal statement itself.

By Ke Zhang, Patricio Gallardo Candela, Sudhir Murthy, Yi Xie, Zhi Wang, Maziar Raissi

agents fine-tuning benchmarks

arXiv AI

Jul 1

Not Every Time and Frequency Need to Be Forgotten in Diffusion Unlearning

arXiv:2510. 17917v2 Announce Type: replace-cross Abstract: Data unlearning aims to remove the influence of specific training samples from a trained model.

By Jinseong Park, Mijung Park

diffusion fine-tuning

arXiv AI

Jul 1

Cross-lingual Relation Extraction with Large Language Models: Zero-Shot, Few-Shot, and Fine-Tuned Evaluation on Romanian

arXiv:2606. 31718v1 Announce Type: cross Abstract: Relation extraction (RE) for low-resource languages is typically constrained by the lack of annotated corpora.

By Dragos-Mitrut Vasile, Elena-Simona Apostol, Stefan-Adrian Toma, Adrian Paschke, Ciprian-Octavian Truica

llms fine-tuning benchmarks

arXiv AI

Jul 1

Rethinking Garment Conditioning in Diffusion-based Virtual Try-On: Decouple, Don't Denoise

arXiv:2511. 18775v2 Announce Type: replace-cross Abstract: Virtual Try-On (VTON) synthesizes realistic images of a person wearing a target garment, with broad applications in e-commerce and fashion.

By Kihyun Na, Jinyoung Choi, Injung Kim

rag diffusion fine-tuning

arXiv AI

Jul 1

Physics-Constrained Fine-Tuning of Flow-Matching Models for Generation and Inverse Problems

arXiv:2508. 09156v3 Announce Type: replace-cross Abstract: We present a framework for fine-tuning flow-matching generative models to enforce physical constraints and solve inverse problems in scientific systems.

By Jan Tauberschmidt, Sophie Fellenz, Sebastian J. Vollmer, Andrew B. Duncan

fine-tuning benchmarks

arXiv Machine Learning

Jul 1

Signed-Permutation Coordinate Transport for RMSNorm Transformers

arXiv:2606. 31963v1 Announce Type: new Abstract: Modern LLM workflows move coordinate-indexed objects across checkpoints: steering vectors, sparse autoencoders, top-$k$ neuron sets, attribution lists, and merge alignments.

By John Sweeney

llms fine-tuning safety

arXiv AI

Jul 1

ReGRPO: Reflection-Augmented Policy Optimization for Tool-Using Agents

arXiv:2606. 31392v1 Announce Type: new Abstract: Tool-augmented vision-language models (VLMs) can solve multimodal, multi-step tasks by calling external tools, yet they remain fragile in practice.

By Binjie Zhang, Mike Zheng Shou

llms agents reinforcement-learning fine-tuning multimodal

arXiv Machine Learning

Jul 1

Probe Choice Changes Canary-Memorization Verdicts: Three Post-Hoc Disagreement Case Studies in a Text-Dominant LoRA-Tuned Autoregressive Testbed

arXiv:2606. 31168v1 Announce Type: cross Abstract: We audit a fixed prefix-window mean-NLL memorization probe (K=20) on a Qwen2.

By Zhichao Fan, Zexin Zhuang, Yanhang Li

fine-tuning

arXiv Machine Learning

Jul 1

A Theory of How Pretraining Shapes Inductive Bias in Fine-Tuning

arXiv:2602. 20062v2 Announce Type: replace Abstract: Pretraining and fine-tuning are central stages in modern machine learning systems.

By Nicolas Anguita, Francesco Locatello, Andrew M. Saxe, Marco Mondelli, Flavia Mancini, Samuel Lippl, Clementine Domine

llms fine-tuning safety

arXiv Machine Learning

Jul 1

When few labeled target data suffice: a theory of semi-supervised domain adaptation via fine-tuning from multiple adaptive starts

arXiv:2507. 14661v2 Announce Type: replace-cross Abstract: Semi-supervised domain adaptation (SSDA) seeks to achieve accurate predictions in a target domain with limited labeled target data by exploiting abundant source and unlabeled target data.

By Wooseok Ha, Yuansi Chen

fine-tuning

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