:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Authors:	Wehner, Jan, Fritz, Mario
Format:	Preprint
Published:	2025
Subjects:	Machine Learning
Online Access:	https://arxiv.org/abs/2510.21531
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Taxonomy, Opportunities, and Challenges of Representation Engineering for Large Language Models
by: Wehner, Jan, et al.
Published: (2025)

Understanding Linear Probing then Fine-tuning Language Models from NTK Perspective
by: Tomihari, Akiyoshi, et al.
Published: (2024)

DAPI: Domain Adaptive Toxicity Probe Vector Intervention for Fine-Grained Detoxification
by: Hyeonsu, Cho, et al.
Published: (2025)

SoK: Reducing the Vulnerability of Fine-tuned Language Models to Membership Inference Attacks
by: Amit, Guy, et al.
Published: (2024)

Memento: Fine-tuning LLM Agents without Fine-tuning LLMs
by: Zhou, Huichi, et al.
Published: (2025)

Can We Use Probing to Better Understand Fine-tuning and Knowledge Distillation of the BERT NLU?
by: Hościłowicz, Jakub, et al.
Published: (2023)

Safe LoRA: the Silver Lining of Reducing Safety Risks when Fine-tuning Large Language Models
by: Hsu, Chia-Yi, et al.
Published: (2024)

HOFT: Householder Orthogonal Fine-tuning
by: Arcas, Alejandro Moreno, et al.
Published: (2025)

Bayesian Fine-tuning in Projected Subspaces
by: Dubovik, Viktar, et al.
Published: (2026)

Open-Vocabulary Calibration for Fine-tuned CLIP
by: Wang, Shuoyuan, et al.
Published: (2024)

Analyzing the Effect of Noise in LLM Fine-tuning
by: Li, Lingfang, et al.
Published: (2026)

A General Framework to Enhance Fine-tuning-based LLM Unlearning
by: Ren, Jie, et al.
Published: (2025)

Automatic Pruning of Fine-tuning Datasets for Transformer-based Language Models
by: Tayaranian, Mohammadreza, et al.
Published: (2024)

Explaining Learned Reward Functions with Counterfactual Trajectories
by: Wehner, Jan, et al.
Published: (2024)

Can Muon Fine-tune Adam-Pretrained Models?
by: Qu, Xingyu, et al.
Published: (2026)

Online Joint Fine-tuning of Multi-Agent Flows
by: Mineiro, Paul
Published: (2024)

Efficient Adjoint Matching for Fine-tuning Diffusion Models
by: Shin, Jeongwoo, et al.
Published: (2026)

Grow, Don't Overwrite: Fine-tuning Without Forgetting
by: Adila, Dyah, et al.
Published: (2026)

BMFT: Achieving Fairness via Bias-based Weight Masking Fine-tuning
by: Xue, Yuyang, et al.
Published: (2024)

ReFine: Boosting Time Series Prediction of Extreme Events by Reweighting and Fine-tuning
by: Shi, Jimeng, et al.
Published: (2024)

Episode-specific Fine-tuning for Metric-based Few-shot Learners with Optimization-based Training
by: Zhuang, Xuanyu, et al.
Published: (2025)

Multitask Mayhem: Unveiling and Mitigating Safety Gaps in LLMs Fine-tuning
by: Jan, Essa, et al.
Published: (2024)

Selective Pre-training for Private Fine-tuning
by: Yu, Da, et al.
Published: (2023)

FDPP: Fine-tune Diffusion Policy with Human Preference
by: Chen, Yuxin, et al.
Published: (2025)

SMART Fine-tuning Factor Augmented Neural Lasso
by: Chai, Jinhang, et al.
Published: (2026)

Efficiently Estimating Data Efficiency for Language Model Fine-tuning
by: Je, Gyung Hyun, et al.
Published: (2025)

A Study of Optimizations for Fine-tuning Large Language Models
by: Singh, Arjun, et al.
Published: (2024)

Understanding Fine-tuning in Approximate Unlearning: A Theoretical Perspective
by: Ding, Meng, et al.
Published: (2024)

Model Balancing Helps Low-data Training and Fine-tuning
by: Liu, Zihang, et al.
Published: (2024)

Functional-level Uncertainty Quantification for Calibrated Fine-tuning on LLMs
by: Niu, Ruijia, et al.
Published: (2024)

Mechanistic Fine-tuning for In-context Learning
by: Cho, Hakaze, et al.
Published: (2025)

Fine-tuning with Very Large Dropout
by: Zhang, Jianyu, et al.
Published: (2024)

$π_\texttt{RL}$: Online RL Fine-tuning for Flow-based Vision-Language-Action Models
by: Chen, Kang, et al.
Published: (2025)

Fine-tuning is Not Fine: Mitigating Backdoor Attacks in GNNs with Limited Clean Data
by: Zhang, Jiale, et al.
Published: (2025)

Entity-level Factual Adaptiveness of Fine-tuning based Abstractive Summarization Models
by: Song, Jongyoon, et al.
Published: (2024)

SEE: Continual Fine-tuning with Sequential Ensemble of Experts
by: Wang, Zhilin, et al.
Published: (2025)

Activated LoRA: Fine-tuned LLMs for Intrinsics
by: Greenewald, Kristjan, et al.
Published: (2025)

Rethinking Safety in LLM Fine-tuning: An Optimization Perspective
by: Kim, Minseon, et al.
Published: (2025)

Aggregating Low Rank Adapters in Federated Fine-tuning
by: Trautmann, Evelyn, et al.
Published: (2025)

Topic Modeling with Fine-tuning LLMs and Bag of Sentences
by: Schneider, Johannes
Published: (2024)