Fine-tuning and adaptation

LoRA, PEFT, instruction tuning and domain adaptation — adapting a pretrained model without paying to train one.

3,943 stories · RSS feed

arXiv AI

Jun 8

Endogenous Resistance to Activation Steering in Language Models

arXiv:2602. 06941v2 Announce Type: replace-cross Abstract: Large language models can recover mid-generation from task-misaligned activation steering, producing explicit verbal restarts (e.

By Alex McKenzie, Keenan Pepper, Stijn Servaes, Martin Leitgab, Murat Cubuktepe, Mike Vaiana, Diogo de Lucena, Judd Rosenblatt, Michael S. A. Graziano

llms robotics fine-tuning safety

arXiv Machine Learning

Jun 8

Closed-Form Spectral Regularization for Multi-Task Model Merging

arXiv:2606. 07289v1 Announce Type: new Abstract: Model merging combines several independently fine-tuned experts into a single multi-task model without any training data, reducing the storage, serving, and decentralized-development costs of large foundation models.

By Yongxian Wei, Runxi Cheng, Xingxuan Zhang, Li Shen, Chun Yuan, Peng Cui, Dacheng Tao

computer-vision fine-tuning multimodal benchmarks

arXiv Machine Learning

Jun 8

TALAN: Task-Aligned Latent Adaptation Networks for Targeted Post-Training of Large Language Models

arXiv:2606. 06902v1 Announce Type: new Abstract: Targeted post-training aims to improve reasoning, math, and code without degrading strengths.

By Chengkai Zhang, Ziteng Liu, Junpu Wang, Zeyi Tao, Yang Wang, Sagar Chordia, Qin Huang

llms fine-tuning benchmarks

arXiv AI

Jun 8

SCALE: Scalable Cross-Attention Learning with Extrapolation for Agentic Workflow Scheduling

arXiv:2606. 06820v1 Announce Type: cross Abstract: Agentic Large Language Model (LLM) systems decompose complex tasks into workflow Directed Acyclic Graphs (DAGs) whose primitives must be scheduled on heterogeneous clusters.

By Zhifei Xu, Jierui Lan, Zixuan Liang, Aiji Liang, Jinxi He

llms agents reinforcement-learning fine-tuning

arXiv Machine Learning

Jun 8

RhinoVLA Technical Report

arXiv:2606. 07383v1 Announce Type: cross Abstract: Vision-Language-Action (VLA) models have shown strong potential for robotic manipulation, but real-time deployment on edge hardware remains challenging.

By Huixi Intelligence, :, Chen Zhang, Chenyang Zhou, Guanglei Ding, Guanghui He, Haibin Gao, Jiajia Chen, Jianyong Zhang, Lianyi Yu, Ningyi Xu, Ping Xu, Qingchen Li, Yingjun Hu, Yijia Zhang, Yuxi Liu

robotics fine-tuning multimodal

arXiv AI

Jun 8

TSAQA: Time Series Analysis Question And Answering Benchmark

arXiv:2601. 23204v2 Announce Type: replace Abstract: Time series data are integral to critical applications across domains such as finance, healthcare, transportation, and environmental science.

By Baoyu Jing, Sanhorn Chen, Lecheng Zheng, Boyu Liu, Zihao Li, Jiaru Zou, Tianxin Wei, Zhining Liu, Zhichen Zeng, Ruizhong Qiu, Xiao Lin, Yuchen Yan, Dongqi Fu, Jingchao Ni, Jingrui He, Hanghang Tong

llms nlp fine-tuning benchmarks

arXiv Machine Learning

Jun 8

Multi-Objective Preference Optimization: Improving Human Alignment of Generative Models

arXiv:2505. 10892v2 Announce Type: replace Abstract: Post-training LLMs with RLHF and preference optimization methods (e.

By Akhil Agnihotri, Rahul Jain, Deepak Ramachandran, Zheng Wen

llms reinforcement-learning fine-tuning benchmarks safety

arXiv AI

Jun 8

Whisper Hallucination Detection and Mitigation via Hidden Representation Steering and Sparse AutoEncoders

arXiv:2606. 07473v1 Announce Type: cross Abstract: Whisper, a widely adopted ASR model, is known to suffer from hallucinations - coherent transcriptions generated for non-speech audio entirely disconnected from the input.

By Georgii Aparin, Vadim Popov, Tasnima Sadekova, Assel Yermekova

fine-tuning multimodal safety

arXiv AI

Jun 8

Superintelligent Retrieval Agent: The Next Frontier of Agentic Retrieval

arXiv:2605. 06647v2 Announce Type: replace-cross Abstract: Retrieval-augmented agents are increasingly the interface to large knowledge bases, yet most treat retrieval as a black box: they issue exploratory queries, inspect snippets, and reformulate until evidence emerges.

By Zeyu Yang, Qi Ma, Jason Chen, Anshumali Shrivastava

llms rag agents reinforcement-learning fine-tuning benchmarks

arXiv Machine Learning

Jun 8

RASFT: Rollout-Adaptive Supervised Fine-Tuning for Reasoning

arXiv:2606. 07006v1 Announce Type: new Abstract: Supervised fine-tuning (SFT) is a prevailing method for adapting large language models to reasoning tasks by imitating offline expert demonstrations, often treating a single expert trajectory as the target behavior.

By Yongliang Miao, Fengyuan Liu, Wei Shi, Yanguang Liu, Fei Sun, Na Zou, Mengnan Du

llms reinforcement-learning fine-tuning benchmarks

arXiv AI

Jun 8

Native3D: End-to-End 3D Scene Generation via Unified Mesh-Texture Modeling and Semantic Alignment

arXiv:2606. 07117v1 Announce Type: cross Abstract: This paper presents Native3D, the first end-to-end 3D scene generation framework that completely bypasses 2D intermediate representations.

By Yibo Liu, Ziwei Zhang, Haozhou Pang, Menghao Li, Lanshan He, Gan Qi

llms diffusion fine-tuning safety

arXiv AI

Jun 8

SlimSearcher: Training Efficiency-Aware Web Agents via Adaptive Reward Gating

arXiv:2606. 07074v1 Announce Type: cross Abstract: Deep research agents have demonstrated remarkable capabilities in complex information-seeking tasks, yet this power comes at a steep computational cost.

By Zequn Xie, Junjie Wang, Dan Yang, Jie Feng, Yue Shen, Jian Wang, Jinjie Gu

agents reinforcement-learning fine-tuning benchmarks safety

arXiv AI

Jun 8

SpectCount: Spectrotemporal Counting via Synthetic Signals Improves Large Audio Language Models

arXiv:2606. 06907v1 Announce Type: cross Abstract: Large audio language models (LALMs) extend large language models with an audio encoder and large-scale audio data.

By Seonuk Kim, Yonghyeon Jun, Ju Yeon Kang, Jimin Hong, Yoonhyeong Lee, Nam Soo Kim

llms fine-tuning benchmarks

Hugging Face Trending Papers

Jun 8

From Hazard Functions to Language Space: Cox-Supervised Distillation of Survival Risk into a Large Language Model

We investigate whether information about time-to-event risk estimated by a Cox proportional hazards model can be transferred into a generative large language model. We propose a text-based survival modelling pipeline in which structured clinical covariates are converted into text prompts and a Qwen-based large language model is fine-tuned to generate patient-specific survival risk using Cox model predictions as a training target.

llms diffusion fine-tuning efficiency

arXiv AI

Jun 6

OneReason Technical Report

arXiv:2606. 06260v1 Announce Type: cross Abstract: Generative recommendation models in the OneRec family have been widely deployed in many real-world services, such as short-video, live-streaming, advertising, and e-commerce.

By OneRec Team, Biao Yang, Boyang Ding, Chenglong Chu, Dunju Zang, Fei Pan, Han Li, Hao Jiang, Honghui Bao, Huanjie Wang, Jian Liang, Jiangxia Cao, Jiao Ou, Jiaxin Deng, Jinghao Zhang, Kun Gai, Lu Ren, Peiru Du, Pengfei Zheng, Rongzhou Zhang, Ruiming Tang, Shiyao Wang, Siyang Mao, Siyuan Lou, Teng Shi, Wei Yuan, Wenlong Xu, Xingchen Liu, Xingmei Wang, Xinqi Jin, Yan Sun, Yan Wang, Yifei Hu, Yingzhi He, Yufei Ye, Yuhao Wang, Yunhao Zhou, Yuqin Dai, Zhao Liu, Zhipeng Wei, Zhixin Ling, Ziming Li, Zixing Zhang, Ziyuan Liu, An Zhang, Changxin Lao, Chaoyi Ma, Chengru Song, Defu Lian, Fan Yang, Guowang Zhang, Hao Peng, Jiayao Shen, Jie Chen, Jun Xu, Junmin Chen, Kun Zhang, Kuo Cai, Mingxing Wen, Minmao Wang, Minxuan Lv, Qi Zhang, Qiang Luo, Sheng Yu, Shijie Li, Shijie Yi, Shuang Yang, Shugui Liu, Shuni Chen, Tinghai Zhang, Tingting Gao, Xiang Wang, Xiangyu Wu, Xiangyu Zhao, Xiao Lv, Xiaoyou Zhou, Xuming Wang, Yong Du, Zejian Zhang, Zhaojie Liu, Zhiyang Zhang, Zhuang Zhuang, Ziqi Wang, Ziyi Zhao

llms reinforcement-learning fine-tuning multimodal

arXiv AI

Jun 6

Noise-Aware Visual Representation Learning for Medical Visual Question Answering

arXiv:2606. 05535v1 Announce Type: cross Abstract: Medical visual question answering (Med-VQA) has strong potential for clinical decision support by enabling AI models to interpret medical images and answer clinically relevant queries.

By I Putu Adi Pratama, Bahadorreza Ofoghi, Atul Sajjanhar, Shang Gao

llms rag nlp fine-tuning benchmarks

arXiv AI

Jun 6

DragOn: A Benchmark and Dataset for Drag-Based GUI Interactions

arXiv:2606. 06322v1 Announce Type: new Abstract: GUI agents - vision-based models that control desktops, web browsers, and mobile devices through graphical user interfaces - promise to automate a wide range of digital tasks.

By Nathan Bout, Maxime Langevin, Ronan Riochet

llms agents robotics fine-tuning multimodal benchmarks

arXiv AI

Jun 6

Multilingual Fine-Tuning via Localized Gradient Conflict Resolution

arXiv:2606. 05613v1 Announce Type: new Abstract: The rapid evolution of Large Language Models (LLMs) has established cross-lingual versatility as a defining feature of modern systems.

By Long P. Hoang, Yiran Zhao, Wei Lu, Wenxuan Zhang

llms fine-tuning

arXiv AI

Jun 6

PerceptUI: LLM Agents as Human-Aligned Synthetic Users for UI/UX Evaluation

arXiv:2606. 05697v1 Announce Type: new Abstract: User interface (UI) and user experience (UX) evaluation is central to product development, yet reliable feedback still relies on recruiting human participants or running online A/B tests, making early-stage iteration slow and costly.

By Nicolas Bougie, Xiaotong Ye, Gian Maria Marconi, Narimasa Watanabe

llms agents fine-tuning multimodal

arXiv AI

Jun 6

TAM: Torque Adaptation Module for Robust Motion Transfer in Manipulation

arXiv:2606. 06218v1 Announce Type: cross Abstract: A policy tuned for one robot often behaves differently on another, whether due to the sim-to-real gap, unknown payloads, or the differing dynamics of two instances of the same robot.

By Dongwon Son, Florian Shkurti, Jason Lee, Naman Shah, Beomjoon Kim, Dieter Fox

reinforcement-learning robotics fine-tuning

Get the daily briefing