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 31

PatchDenoiser: Parameter-efficient multi-scale patch learning and fusion denoiser for Low-dose CT imaging

arXiv:2602. 21987v3 Announce Type: replace-cross Abstract: Low-dose CT images are essential for reducing radiation exposure in cancer screening, pediatric imaging, and longitudinal monitoring protocols, but their quality is often degraded by noise from low-dose acquisition, patient motion, or scanner limitations, affecting both clinical interpretation and downstream analysis.

By Jitindra Fartiyal, Pedro Freire, Sergei K. Turitsyn, Sergei G. Solovski

llms diffusion fine-tuning benchmarks

arXiv Machine Learning

Jul 31

GVR-Coder: A Visual-Feedback Framework for Structured SVG Generation in Complex Document and Meeting Scenarios

arXiv:2607. 28073v1 Announce Type: new Abstract: In demanding professional environments and meeting review scenarios, lengthy text often imposes a high cognitive load.

By Yiming Xu, Jihua Kang, Chunsai Du, Qifan Zhang, Wangqiu Zhou, Yiting Wu, Tianqi Li, Qi Song

agents reinforcement-learning fine-tuning

arXiv AI

Jul 31

Predict before you train: Scaling Laws for particle physics foundation models

arXiv:2607. 23377v1 Announce Type: cross Abstract: The largest machine learning models in particle physics are also the most expensive to train, yet the return on scaling a given architecture cannot be estimated before that compute is spent.

By Jan-Lucas Uslu, Benjamin Nachman, Christopher Re

llms fine-tuning benchmarks

arXiv Machine Learning

Jul 31

S-GRPO: Unified Post-Training for Large Vision-Language Models

arXiv:2604. 16557v2 Announce Type: replace Abstract: Current post-training methodologies for adapting Large Vision-Language Models (LVLMs) generally fall into two paradigms: Supervised Fine-Tuning (SFT) and Reinforcement Learning (RL).

By Yuming Yan, Kai Tang, Sihong Chen, Ke Xu, Dan Hu, Qun Yu, Pengfei Hu

llms reinforcement-learning fine-tuning multimodal

arXiv Machine Learning

Jul 31

Subtract or Replay? Exact Deletion from Language-Model Memory

arXiv:2607. 27539v1 Announce Type: new Abstract: Exact deletion from persistent language-model memory depends on how that memory represents a record.

By Vishwajith Ramesh

fine-tuning

arXiv Machine Learning

Jul 31

RLPF: Reinforcement Learning from Performance Feedback for Code Generation

arXiv:2607. 27271v1 Announce Type: new Abstract: Code models are increasingly trained with execution feedback, but most training signals still stop at correctness.

By Huihao Jing, Haozhe Cui, Wenbin Hu, Shaojin Chen, Haochen Shi, Changxuan Fan, Yuxuan Liu, Hanyu Yang, Sirui Zhang, Ziyi Chen, Haoran Li, Yangqiu Song

agents reinforcement-learning fine-tuning benchmarks

arXiv Machine Learning

Jul 31

SDO: Structure-Aware Data Organization for Efficient LLM Post-Training

arXiv:2607. 27273v1 Announce Type: new Abstract: Post-training of large language models is expensive, and existing efficiency improvements mainly focus on selecting informative samples or designing training schedules.

By Jinliang Gao, Ning Yang, Hai Wang, Baili Xiao, Pin Lyu

llms rag reinforcement-learning fine-tuning

arXiv Machine Learning

Jul 31

Region-adaptable retrieval of coastal biogeochemical parameters from near-surface hyperspectral remote sensing reflectance using physics-aware meta-learning

arXiv:2605. 05623v2 Announce Type: replace Abstract: Hyperspectral in situ sensing has shown promise in retrieving aquatic biogeochemical (BGC) parameters, such as total suspended solids, dissolved organic carbon, and total chlorophyll-a, for cost-effective monitoring of coastal water quality.

By Yiqing Guo, Nagur R. C. Cherukuru, Eric A. Lehmann, S. L. Kesav Unnithan, Tim J. Malthus, Gemma Kerrisk, Xiubin Qi, Faisal Islam, Tisham Dhar, Mark J. Doubell

fine-tuning benchmarks

arXiv Machine Learning

Jul 31

TriShield: Zero-Utility-Loss Defense Against Privacy Backdoors in Federated Language Model Fine-Tuning via Orthogonal Gradient Projection and Optimizer State Entanglement

arXiv:2607. 27940v1 Announce Type: new Abstract: Federated fine-tuning of large language models (LLMs) enables collaborative training without exposing raw data.

By Cheng Wei (Honor Device Co., Ltd., Shenzhen, China)

llms fine-tuning safety

arXiv Machine Learning

Jul 31

REFINE-DP: Diffusion Policy Fine-tuning for Humanoid Loco-manipulation via Reinforcement Learning

arXiv:2603. 13707v3 Announce Type: replace-cross Abstract: Humanoid loco-manipulation requires coordinated task-space motion planning with stable loco-manipulation command tracking under complex robot-environment dynamics and long-horizon tasks.

By Zhaoyuan Gu, Yipu Chen, Zimeng Chai, Alfred Cueva, Thong Nguyen, Yifan Wu, Huishu Xue, Minji Kim, Isaac Legene, Fukang Liu, KyoungMok Kim, Ayan Barula, Yongxin Chen, Ye Zhao

diffusion reinforcement-learning robotics fine-tuning

arXiv Machine Learning

Jul 31

Compliance2LoRA: On-Demand Safety Alignment on Arbitrary Policy Subsets via Hypernetwork-Generated LoRA Adapters

arXiv:2607. 27594v1 Announce Type: new Abstract: Post-training alignment in large reasoning models (LRMs) has significantly improved their adaptability to diverse safety compliance settings.

By Pankayaraj Pathmanathan, Furong Huang

fine-tuning safety

arXiv Machine Learning

Jul 31

Gradient-free Task-Conditioned Retrieval for On-Device In-Context Learning

arXiv:2607. 27766v1 Announce Type: cross Abstract: On-device in-context learning (ICL) relies on pre-inference retrieval to select demonstrations for useful context before downstream model inference.

By Xinyu Luo, Hui Liu, Yihua Shao, Junyi Yang, Arindam Basu, Haoliang Li

llms fine-tuning multimodal benchmarks safety

arXiv Machine Learning

Jul 31

VESTIGE: A Knowledge-Guided Masking Strategy for Corruption-Aware Fine-Tuning of Genomic Transformers, Validated on Ancient DNA Reconstruction

arXiv:2607. 27712v1 Announce Type: new Abstract: Standard masked-language-model fine-tuning applies a uniform masking probability across every token position, assuming reconstruction difficulty is position-agnostic.

By Angshuman Chakravertty, Rahul Maheshwari

llms fine-tuning

arXiv Machine Learning

Jul 31

ShadowDancer: Teaching Video World Models Any Action by Learning Unified Dynamics Representations from a Video and Its Shadow

arXiv:2607. 28362v1 Announce Type: cross Abstract: We present ShadowDancer, a novel approach to any-action, frame-level control of interactive video world models.

By Jin Cao, Zian Meng, Kaipeng Zhang

fine-tuning

arXiv AI

Jul 31

Progressive Multimodal Alignment for Continual Instruction Tuning

arXiv:2607. 26947v1 Announce Type: cross Abstract: Multimodal Large Language Models (MLLMs) rely on a projector to align visual representations with the language embedding space, making it central to cross-modal understanding.

By Duzhen Zhang, Yahan Yu, Qiaoyi Su, Jiahua Dong, Tielin Zhang

llms rag fine-tuning multimodal benchmarks safety

arXiv AI

Jul 31

Contextualized Counterspeech Can Be More Persuasive Than Generic Counterspeech

arXiv:2607. 26236v1 Announce Type: cross Abstract: AI-generated counterspeech offers a scalable and effective strategy to mitigate online toxicity by promoting more constructive dialogue.

By Lorenzo Cima, Alessio Miaschi, Amaury Trujillo, Marco Avenuti, Felice Dell'Orletta, Stefano Cresci

fine-tuning safety

arXiv Machine Learning

Jul 31

A Lightweight Foundation Model for Collider Physics with Multi-Domain Adaptation

arXiv:2607. 27501v1 Announce Type: new Abstract: We present a lightweight approach to foundation modeling (\textbf{NEXUS}) that leverages pre-trained learning from collider physics data towards out-of-domain tasks in other scientific datasets, using a fully connected autoencoder model with approximately 3 million parameters.

By Liangyu Wu, Qibin Liu, Alexander Yue, Julia Gonski

llms diffusion fine-tuning

arXiv AI

Jul 31

Probing the Origins of Reasoning Performance: Representational Quality for Mathematical Problem-Solving in RL vs. SFT Fine-Tuned Models

arXiv:2607. 26119v1 Announce Type: new Abstract: Large reasoning models trained via reinforcement learning (RL) have been increasingly shown to outperform their supervised fine-tuned (SFT) counterparts on mathematical reasoning tasks; Yet the mechanistic basis for this advantage remains unclear.

By Antyabha Rahman, Akshaj Gurugubelli, Omar Ankit, Kevin Zhu, Aishwarya Balwani

reinforcement-learning fine-tuning benchmarks

arXiv Machine Learning

Jul 31

LoRA Scaffolded Policy Optimization (LSPO): A Sampling-Time Low-Rank Scaffold for Recovering Reinforcement-Learning Gradient on Zero-Reward Cliff Prompts

arXiv:2607. 27787v1 Announce Type: new Abstract: Reinforcement learning from verifiable rewards (RLVR) for mathematical reasoning suffers from a structural blind spot: on "cliff" prompts-those on which every sampled rollout in a group fails-the group-normalized advantage is identically zero, so GRPO produces no gradient on precisely the prompts at the frontier of the model's capability.

By Ken Ding

reinforcement-learning fine-tuning benchmarks

arXiv AI

Jul 31

ScratchSim: A Procedural Synthetic Data Pipeline for Surface Scratch Detection

arXiv:2607. 27065v2 Announce Type: cross Abstract: While automated defect detection such as the detection of surface scratched is an important aspect in industrial quality control, the scarcity of annotated defect data make this task challenging.

By Paul Julius K\"uhn, Saptarshi Neil Sinha, Tiago Kleist, Richard Hoffmann, Arjan kuijper, Michael Weinmann

llms fine-tuning data-engineering

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