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 2

Towards Sparse Video Understanding and Reasoning

arXiv:2602. 13602v2 Announce Type: replace-cross Abstract: We present \revise (\underline{Re}asoning with \underline{Vi}deo \underline{S}parsity), a multi-round agent for video question answering (VQA).

By Chenwei Xu, Zhen Ye, Shang Wu, Weijian Li, Zihan Wang, Zhuofan Xia, Lie Lu, Pranav Maneriker, Fan Du, Manling Li, Han Liu

llms agents nlp fine-tuning multimodal benchmarks

arXiv AI

Jun 2

ACON: Optimizing Context Compression for Long-horizon LLM Agents

arXiv:2510. 00615v3 Announce Type: replace Abstract: Large language models (LLMs) are increasingly deployed as agents in dynamic real-world environments, where success depends on maintaining precise records of actions and observations.

By Minki Kang, Wei-Ning Chen, Dongge Han, Huseyin A. Inan, Lukas Wutschitz, Yanzhi Chen, Robert Sim, Saravan Rajmohan

llms agents fine-tuning

arXiv AI

Jun 2

Cross-modal linkage risk in clinical vision-language models

arXiv:2606. 02276v1 Announce Type: cross Abstract: Vision-language models (VLMs) trained on paired chest radiographs and radiology reports learn a shared embedding space that can preserve instance-level image-report correspondence.

By Soroosh Tayebi Arasteh, Mahshad Lotfinia, Sven Nebelung, Daniel Truhn

llms rag fine-tuning multimodal benchmarks safety

arXiv Machine Learning

Jun 2

RefLoRA: Refactored Low-Rank Adaptation for Efficient Fine-Tuning of Large Models

arXiv:2505. 18877v4 Announce Type: replace Abstract: Low-Rank Adaptation (LoRA) lowers the computational and memory overhead of fine-tuning large models by updating a low-dimensional subspace of the pre-trained weight matrix.

By Yilang Zhang, Bingcong Li, Georgios B. Giannakis

llms fine-tuning benchmarks

arXiv AI

Jun 2

LinguIUTics at PsyDefDetect: Iterative Imbalance-Aware Fine-tuning of Qwen3-8B for Psychological Defense Mechanism Classification

arXiv:2606. 00647v1 Announce Type: cross Abstract: Detecting psychological defense mechanisms in conversational text remains a challenging clinical NLP problem.

By Shefayat E Shams Adib, Ahmed Alfey Sani, Md Hasibur Rahman Alif, Ajwad Abrar

llms nlp fine-tuning benchmarks safety

arXiv AI

Jun 2

An Open-Source Benchmark and Baseline for Multi-temporal Referring Segmentation

arXiv:2606. 00987v1 Announce Type: cross Abstract: Large Vision-Language Models (LVLMs) have shown strong visual understanding and language-guided grounding abilities, yet their capacity for multi-temporal visual reasoning remains underexplored.

By Bingyu Li, Da Zhang, Tao Huo, Zhiyuan Zhao, Junyu Gao, Xuelong Li

llms agents computer-vision fine-tuning multimodal benchmarks

arXiv AI

Jun 2

CSRP: Chain-of-Thought Reasoning for Chinese Text Correction via Reinforcement Learning with Efficiency-Aware Rewards

arXiv:2606. 00020v1 Announce Type: cross Abstract: Large Language Model (LLM) based Chinese Grammatical Error Correction (CGEC) systems face two critical challenges: general-purpose models lack specialized linguistic priors for subtle grammatical distinctions, and Supervised Fine-Tuning (SFT) with Maximum Likelihood Estimation fails to optimize for precision-focused metrics, leading to systematic over-correction.

By Wei Tian, Yuhao Zhou, Man Lan

llms nlp reinforcement-learning fine-tuning benchmarks safety

arXiv Machine Learning

Jun 2

From Reward-Free Representations to Preferences: Rethinking Offline Preference-Based Reinforcement Learning

arXiv:2606. 01123v1 Announce Type: new Abstract: Preference-based reinforcement learning (PbRL) avoids explicit reward engineering by learning from pairwise human preference feedback.

By Jun-Jie Yang, Chia-Heng Hsu, Kui-Yuan Chen, Ping-Chun Hsieh

reinforcement-learning fine-tuning

arXiv AI

Jun 2

REAL: Resolving Knowledge Conflicts in Knowledge-Intensive Visual Question Answering via Reasoning-Pivot Alignment

arXiv:2602. 14065v2 Announce Type: replace Abstract: Knowledge-intensive Visual Question Answering (KI-VQA) frequently suffers from severe knowledge conflicts caused by the inherent limitations of open-domain retrieval.

By Kai Ye, Xianwei Mao, Sheng Zhou, Zirui Shao, Ye Mo, Liangliang Liu, Haikuan Huang, Bin Li, Jiajun Bu

nlp fine-tuning safety

arXiv AI

Jun 2

Parameter-Efficient Fine-Tuning of Large Pretrained Models for Instance Segmentation Tasks

arXiv:2606. 01947v1 Announce Type: cross Abstract: Research and applications in artificial intelligence have recently shifted with the rise of large pretrained models, which deliver state-of-the-art results across numerous tasks.

By Nermeen Abou Baker, David Rohrschneider, Uwe Handmann

llms computer-vision fine-tuning benchmarks

arXiv AI

Jun 2

TInR: Exploring Tool-Internalized Reasoning in Large Language Models

arXiv:2604. 10788v2 Announce Type: replace-cross Abstract: Tool-Integrated Reasoning (TIR) has emerged as a promising direction by extending Large Language Models' (LLMs) capabilities with external tools during reasoning.

By Qiancheng Xu, Yongqi Li, Fan Liu, Hongru Wang, Min Yang, Wenjie Li

llms reinforcement-learning fine-tuning safety

arXiv Machine Learning

Jun 2

Learning To Sample From Diffusion Models Via Inverse Reinforcement Learning

arXiv:2602. 08689v2 Announce Type: replace Abstract: Diffusion models generate samples through an iterative denoising process guided by a pretrained neural network.

By Constant Bourdrez, Alexandre V\'erine, Olivier Capp\'e

diffusion reinforcement-learning fine-tuning

arXiv AI

Jun 2

Fair Finetuning Mitigates Distribution Inference Attacks

arXiv:2606. 01719v1 Announce Type: cross Abstract: Machine learning models trained on sensitive data can inadvertently leak population-level information about their training distributions -- a threat known as distribution inference attack (DIA).

By Rakshit Naidu

nlp fine-tuning safety

arXiv AI

Jun 2

RLVR without Ineffective Samples: Group Prioritized Off-Policy Optimization for LLM Reasoning

arXiv:2606. 01281v1 Announce Type: cross Abstract: Reinforcement learning with verifiable rewards (RLVR) has emerged as a powerful paradigm for enhancing the reasoning capabilities of large language models (LLMs).

By Yixiu Mao, Yun Qu, Qi Wang, Heming Zou, Xiangyang Ji

llms reinforcement-learning fine-tuning benchmarks safety

arXiv Machine Learning

Jun 2

PRISM: Preference-Aware Influence Function Based Data Selection Method for Efficient Fine-Tuning

arXiv:2605. 21422v3 Announce Type: replace Abstract: As LLMs continue to scale up, improving training efficiency heavily relies on effective data utilization.

By Qihao Lin, Guanxu Chen, Dongrui Liu, Jing Shao

llms fine-tuning safety

arXiv AI

Jun 2

Fine-Tuning Diffusion Models for Molecular Generation via Reinforcement Learning and Fast Sampling

arXiv:2606. 01220v1 Announce Type: cross Abstract: Generating molecules that simultaneously satisfy drug-like properties and conform to the 3D structure of a target protein is a core challenge in structure-based drug design (SBDD).

By Guang Lin, Shikui Tu, Lei Xu

diffusion reinforcement-learning fine-tuning benchmarks data-engineering

arXiv Machine Learning

Jun 2

Stabilizing Policy Optimization via Logits Convexity

arXiv:2603. 00963v2 Announce Type: replace Abstract: While reinforcement learning (RL) has been central to the recent success of large language models (LLMs), RL optimization is notoriously unstable, especially when compared to supervised fine-tuning (SFT).

By Hongzhan Chen, Tao Yang, Yuhua Zhu, Shiping Gao, Xiaojun Quan, Ting Yao

llms reinforcement-learning fine-tuning benchmarks

arXiv Machine Learning

Jun 2

DVD: Discrete Voxel Diffusion for 3D Generation and Editing

arXiv:2605. 07971v2 Announce Type: replace-cross Abstract: We introduce Discrete Voxel Diffusion (DVD), a discrete diffusion framework to generate, assess, and edit sparse voxels for SLat (Structured LATent) based 3D generative pipelines.

By Zhengrui Xiang, Jiaqi Wu, Fupeng Sun, Heliang Zheng, Yingzhen Li

diffusion fine-tuning

arXiv Machine Learning

Jun 2

Neural Low-Discrepancy Sequences

arXiv:2510. 03745v2 Announce Type: replace Abstract: Low-discrepancy points are designed to efficiently fill the space in a uniform manner.

By Michael Etienne Van Huffel, Nathan Kirk, Makram Chahine, Daniela Rus, T. Konstantin Rusch

computer-vision robotics fine-tuning

arXiv AI

Jun 2

Ryze: Evidence-Enriched Data Synthesis from Biomedical Papers

arXiv:2606. 00902v1 Announce Type: new Abstract: General-purpose VLMs remain unreliable for biomedical research because valid answers in scientific papers depend on evidence split across figures, tables, charts, captions, and referring text.

By Yeqi Huang, Yue Chen, Yanwei Ye, Guanhao Su, Luo Mai

llms computer-vision reinforcement-learning fine-tuning multimodal

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