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 Machine Learning

Jun 9

The Sample Complexity of Parameter-Free Stochastic Convex Optimization

arXiv:2506. 11336v2 Announce Type: replace Abstract: We study the sample complexity of stochastic convex optimization when problem parameters such as the distance to optimality and the Lipschitz constant are unknown.

By Jared Lawrence, Ari Kalinsky, Hannah Bradfield, Yair Carmon, Oliver Hinder

llms fine-tuning

arXiv Machine Learning

Jun 9

In-Context Learning of Stochastic Differential Equations with Foundation Inference Models

arXiv:2502. 19049v3 Announce Type: replace Abstract: Stochastic differential equations (SDEs) describe dynamical systems where deterministic flows, governed by a drift function, are superimposed with random fluctuations, dictated by a diffusion function.

By Patrick Seifner, Kostadin Cvejoski, David Berghaus, Cesar Ojeda, Ramses J. Sanchez

llms diffusion fine-tuning

arXiv Machine Learning

Jun 9

Zero and Few Shot Load Forecasting with Large Language Models

arXiv:2411. 11350v2 Announce Type: replace Abstract: Deep learning models have shown strong performance in load forecasting, but they generally require large amounts of data for model training before being applied to new scenarios, which limits their effectiveness in data-scarce scenarios.

By Wenlong Liao, Chengrui Zhang, Zhe Yang, Mengshuo Jia, Christian Rehtanz, Jiannong Fang, Fernando Port\'e-Agel

llms nlp fine-tuning

arXiv Machine Learning

Jun 9

GNSS-FM: A Self-Supervised Foundation Model for Daily GNSS Displacement Time Series

arXiv:2606. 07725v1 Announce Type: cross Abstract: Displacement time series from Global Navigation Satellite Systems (GNSS) are essential for a wide range of applications, including monitoring tectonic crustal deformations and investigating the different stages of the earthquake cycle.

By Nick Teutschmann (Institute of Geodesy and Photogrammetry, ETH Zurich, Switzerland), Laura Crocetti (Institute of Geodesy and Photogrammetry, ETH Zurich, Switzerland), Fanny Lehmann (ETH AI Center, Switzerland), Leonardo Trentini (Institute of Geodesy and Photogrammetry, ETH Zurich, Switzerland), Benedikt Soja (Institute of Geodesy and Photogrammetry, ETH Zurich, Switzerland)

fine-tuning efficiency

arXiv Machine Learning

Jun 9

Need We Teach Foundation Models What is a Generative Image? Gradient-Free Generative Artifact Detection via Analytic Spectral Adaptation

arXiv:2606. 07660v1 Announce Type: cross Abstract: Adapting foundation models to detect generative artifacts via gradient-based updates compromises their intrinsic representations.

By Qiaoyu Chen, Bing Zhang

diffusion fine-tuning safety

arXiv Machine Learning

Jun 9

Distilling Safe LLM Systems via Soft Prompts for On Device Settings

arXiv:2606. 09388v1 Announce Type: new Abstract: Deploying safe large language models (LLMs) on resource-constrained edge devices presents a critical challenge: while dual-model systems combining LLMs with guard models provide effective safety guarantees, their substantial memory and computational demands make them prohibitively expensive for on-device deployment.

By Motasem Alfarra, Cristina Pinneri, Dana Kianfar, Mohammed Almousa, Christos Louizos

llms fine-tuning efficiency benchmarks safety

arXiv Machine Learning

Jun 9

Lost in the Non-convex Loss Landscape: How to Fine-tune the Large Time Series Model?

arXiv:2606. 08578v1 Announce Type: new Abstract: Recently, large time series models (LTSMs) have gained increasing attention due to their similarities to large language models, including flexible context length, scalability, and task generality, outperforming advanced task-specific models.

By Xu Zhang, Peang Wang, Wei Wang

llms fine-tuning benchmarks

arXiv Machine Learning

Jun 9

De novo molecular generation with optical property preconditioning at the token level

arXiv:2606. 08221v1 Announce Type: new Abstract: Designing OLED molecules with targeted optical properties remains challenging due to the scarcity of high-quality data and the limited reliability of conditional control in generative models across chemical motifs.

By Haozhe Huang, Manuel Gonzalez Lastre, Hyun Suk Park, Jorge A. Campos-Gonzalez-Angulo, Xinjian Liu, Al\'an Aspuru-Guzik

llms fine-tuning benchmarks

arXiv Machine Learning

Jun 9

Have I Solved This Before? Retrieving Similar Segmentation Problems for Evolutionary Learning

arXiv:2606. 08155v1 Announce Type: new Abstract: Reliable integration and solid configuration of monitoring systems constitute a fundamental prerequisites for achieving high efficiency and productivity in contemporary manufacturing environments.

By Andreas Margraf, Henning Cui, J\"org H\"ahner

computer-vision fine-tuning

arXiv Machine Learning

Jun 9

AttentionCap: Transformer Based Capacitance Matrix Learning Toward Full-Chip Extraction

arXiv:2606. 08161v1 Announce Type: new Abstract: As capacitance extraction accuracy of rule-based pattern matching becomes difficult to sustain at advanced nodes, a growing trend emerges to develop deep-learning-based 2D capacitance models.

By Jiechen Huang, Hector R. Rodriguez, Dingcheng Yang, Zuochang Ye, Yibo Lin, Wenjian Yu

llms rag fine-tuning efficiency

arXiv AI

Jun 9

S3Mem: Structured Spatiotemporal Scene-Event Memory for Long-Horizon Interactive Question Answering

arXiv:2605. 28831v2 Announce Type: replace-cross Abstract: Long-horizon memory question answering often requires sparse evidence from heterogeneous histories, including events, object states, visual observations, temporal relations, and causal steps.

By Encheng Su, Jianyu Wu, Jinouwen Zhang, Qiucheng Yu, Chen Tang, Pengze Li, Lintao Wang, Aoran Wang, Xinzhu Ma, Shixiang Tang, Yizhou Wang, Houqiang Li

agents nlp fine-tuning

arXiv AI

Jun 9

CrossVLA: Cross-Paradigm Post-Training and Inference Optimization for Vision-Language-Action Models

arXiv:2605. 21854v2 Announce Type: replace-cross Abstract: Vision-Language-Action (VLA) models have rapidly converged on a small set of architectural patterns: discrete-token autoregression (e.

By Zhi Liu

llms reinforcement-learning fine-tuning multimodal benchmarks safety

arXiv AI

Jun 9

AgroOmni: A Large-Scale Multi-view Agricultural Dataset for Cross-Scale Multimodal Reasoning

arXiv:2603. 14342v2 Announce Type: replace-cross Abstract: Modern agricultural data is sourced from diverse platforms and spans multiple spatial scales, ranging from ground-level close-up photography to Unmanned Aerial Vehicle (UAV) aerial observation and satellite remote sensing imagery.

By Jiarui Zhang, Junqi Hu, Zurong Mai, Yang Liu, Yuhang Chen, Shuohong Lou, Henglian Huang, Hong Cheng, Lingyuan Zhao, Jianxi Huang, Yutong Lu, Haohuan Fu, Juepeng Zheng

llms nlp fine-tuning multimodal benchmarks safety

arXiv AI

Jun 9

Generative Reasoning Re-ranker

arXiv:2602. 07774v5 Announce Type: replace-cross Abstract: Recent studies increasingly explore Large Language Models (LLMs) as a new paradigm for recommendation systems due to their scalability and world knowledge.

By Mingfu Liang, Yufei Li, Jay Xu, Kavosh Asadi, Xi Liu, Shuo Gu, Kaushik Rangadurai, Frank Shyu, Shuaiwen Wang, Song Yang, Zhijing Li, Jiang Liu, Mengying Sun, Fei Tian, Xiaohan Wei, Chonglin Sun, Jacob Tao, Shike Mei, Wenlin Chen, Santanu Kolay, Sandeep Pandey, Hamed Firooz, Luke Simon

llms rag nlp reinforcement-learning fine-tuning benchmarks

arXiv AI

Jun 9

Large Language Models for Imbalanced Classification: Diversity makes the difference

arXiv:2510. 09783v2 Announce Type: replace-cross Abstract: Oversampling is one of the most widely used approaches for addressing imbalanced classification.

By Dang Nguyen, Sunil Gupta, Kien Do, Thin Nguyen, Taylor Braund, Alexis Whitton, Svetha Venkatesh

llms fine-tuning benchmarks

arXiv AI

Jun 9

Thinking-Based Non-Thinking: Solving the Reward Hacking Problem in Training Hybrid Reasoning Models via Reinforcement Learning

arXiv:2601. 04805v2 Announce Type: replace Abstract: Large reasoning models (LRMs) have attracted much attention due to their exceptional performance.

By Siyuan Gan, Jiaheng Liu, Boyan Wang, Tianpei Yang, Runqing Miao, Yuyao Zhang, Fanyu Meng, Junlan Feng, Linjian Meng, Jing Huo, Yang Gao

reinforcement-learning fine-tuning benchmarks

arXiv AI

Jun 9

Data Synthesis and Parameter-Efficient Fine-Tuning for Low-Resource NMT: A Case Study on Q'eqchi' Mayan

arXiv:2606. 09767v1 Announce Type: cross Abstract: Neural machine translation for digitally low-resource Indigenous languages is often hindered by extreme data scarcity, prompting reliance on extractive web-scraping.

By Alexander Chulzhanov, Soeren Eberhardt, Arjun Mukherjee

llms nlp fine-tuning benchmarks

arXiv AI

Jun 9

Muon Learns More Robust and Transferable Features than Adam

arXiv:2606. 09658v1 Announce Type: cross Abstract: Muon has recently emerged as a state-of-the-art optimizer for pretraining Large Language Models (LLMs) and vision classifiers.

By Tianyu Ruan, Fengzhuo Zhang, Shuche Wang, Shihua Zhang

llms fine-tuning benchmarks

arXiv AI

Jun 9

Beyond Humans: Multispecies Animal Face Recognition Using Transfer Learning

arXiv:2606. 09353v1 Announce Type: cross Abstract: Individual animal recognition can be useful in the search for lost or stolen pets, the tracking of individuals of endangered species, and the recognition of animals in crowded farms.

By Maria De Marsico, Anil K. Jain, Annalaura Miglino

llms computer-vision fine-tuning benchmarks

arXiv AI

Jun 9

Decoding Pedestrian Crossing Intention from Egocentric Vision via Vision Language Models

arXiv:2606. 09142v1 Announce Type: cross Abstract: Egocentric vision offers a first-person view of human perception and decision making, yet its potential for traffic-safety prediction remains underexplored.

By Danya Li, Xiang Su, Yan Feng, Rico Krueger

llms nlp fine-tuning benchmarks safety

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