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 17

Stop Thinking, Start Looking: Efficient Post-Training for Multimodal Document Question Answering via Reasoning-Free Alignment

arXiv:2607. 14682v1 Announce Type: new Abstract: Efficient multimodal document question answering with explicit visual grounding, locating the precise document region that supports each answer remains an open challenge.

By Harikrishnan P M, Goutham Vignesh, Ganesh Parab, Saisubramaniam Gopalakrishnan, Vishal Vaddina, Varun V, Rohit Agrawal

nlp reinforcement-learning fine-tuning multimodal benchmarks safety

arXiv Machine Learning

Jul 17

On-Policy Delta Distillation

arXiv:2607. 15161v1 Announce Type: new Abstract: On-policy distillation is an alternative post-training method in reinforcement learning that alleviates the constraints imposed by reward models by providing token-level supervision from a teacher model.

By Byeongho Heo, Jaehui Hwang, Sangdoo Yun, Dongyoon Han

llms reinforcement-learning fine-tuning efficiency benchmarks

arXiv AI

Jul 17

Non-vacuous Generalization Bounds for Reinforcement Learning with Verifiable Rewards

arXiv:2607. 14506v1 Announce Type: cross Abstract: While reinforcement learning with verifiable rewards (RLVR) is widely used to improve the reasoning capabilities of large language models (LLMs), the generalizability of the resulting models remains poorly understood.

By Yuxuan Zhu, Rohan Alur, Daniel Kang

llms reinforcement-learning fine-tuning efficiency data-engineering

arXiv AI

Jul 17

QDA-SQL: Questions Enhanced Dialogue Augmentation for Multi-Turn Text-to-SQL

arXiv:2406. 10593v3 Announce Type: replace Abstract: Fine-tuning large language models (LLMs) for specific domain tasks has achieved great success in Text-to-SQL tasks.

By Yinggang Sun, Ziming Guo, Haining Yu, Chuanyi Liu, Xiang Li, Bingxuan Wang, Xiangzhan Yu, Tiancheng Zhao

llms fine-tuning data-engineering

arXiv Machine Learning

Jul 17

XCT-SAM: Sequential Parameter-Efficient Domain Adaptation of SAM for Industrial XCT Defect Segmentation

arXiv:2607. 14287v1 Announce Type: cross Abstract: Defect segmentation in additive manufacturing (AM) X-ray computed tomography (XCT) images remains challenging due to severe class imbalance and large distribution shifts across scan conditions.

By Md Mahedi Hasan, Md Mushfiqur Rahaman, Alan Pachkovskiy, Imtiaz Ahmed, Jeremy Dawson, Srinjoy Das

computer-vision fine-tuning benchmarks safety

arXiv AI

Jul 17

Reachability-Aware Pretraining for Efficient Target-Oriented Path Exploration in Temporal Knowledge Graph Reasoning

arXiv:2607. 14886v1 Announce Type: new Abstract: Temporal Knowledge Graph (TKG) reasoning under the extrapolation setting focuses on forecasting future time-stamped events (facts) from historical data in a temporal knowledge graph.

By Chien-Liang Liu, Tsao-Lun Chen

agents reinforcement-learning fine-tuning safety

arXiv AI

Jul 16

RADAR: Closed-Loop Robotic Data Generation via Semantic Planning and Autonomous Causal Environment Reset

arXiv:2603. 11811v2 Announce Type: replace-cross Abstract: The acquisition of large-scale physical interaction data, a critical prerequisite for modern robot learning, is severely bottlenecked by the prohibitive cost and scalability limits of human-in-the-loop collection paradigms.

By Yongzhong Wang, Keyu Zhu, Yong Zhong, Liqiong Wang, Jinyu Yang, Feng Zheng

llms agents nlp robotics fine-tuning multimodal

arXiv Machine Learning

Jul 16

Pretraining in Actor-Critic Reinforcement Learning for Locomotion

arXiv:2510. 12363v4 Announce Type: replace-cross Abstract: The pretraining-finetuning paradigm has facilitated numerous transformative advancements in artificial intelligence research in recent years.

By Jiale Fan, Andrei Cramariuc, Tifanny Portela, Marco Hutter

reinforcement-learning robotics fine-tuning benchmarks

arXiv Machine Learning

Jul 16

BenthiCat: An opti-acoustic dataset for advancing benthic classification and habitat mapping

arXiv:2510. 04876v3 Announce Type: replace-cross Abstract: Benthic habitat mapping is fundamental for understanding marine ecosystems, guiding conservation efforts, and supporting sustainable resource management.

By Hayat Rajani, Valerio Franchi, Borja Martinez-Clavel Valles, Raimon Ramos, Rafael Garcia, Nuno Gracias

agents computer-vision fine-tuning multimodal benchmarks

arXiv AI

Jul 16

Barnamala: Parameter-Efficient Handwritten Devanagari Recognition at Benchmark Saturation

arXiv:2607. 13689v1 Announce Type: cross Abstract: We built a compact convolutional network (1.

By Ashish Thapa, Samrat Karki

fine-tuning efficiency benchmarks

arXiv Machine Learning

Jul 16

EXPLORE: Exploration with Guided Search for Analog Topology Generation using Language Models

arXiv:2607. 13416v1 Announce Type: new Abstract: Automating analog circuit topology design is essential to reduce the extensive manual effort required to meet increasingly diverse and customized application demands.

By Guanglei Zhou, Chen-Chia Chang, Yikang Shen, Jonathan Ku, Isaac Jacobson, Jingyu Pan, Yiran Chen, Xin Zhang

llms fine-tuning benchmarks

arXiv Machine Learning

Jul 16

Delving into the Temporal Challenges of Unified Video Protection Against Image-to-Video and Fine-Tuning-based Customization

arXiv:2607. 13336v1 Announce Type: cross Abstract: Recent diffusion-based video generation models have enabled high-quality personalized video customization through both tuning-based pipelines, which fine-tune a video diffusion model, and reference-based pipelines such as image-to-video generation.

By Yuxin Huang, Ziming Hong, Mingming Gong, Wanyu Wang, Jing Zhang, Tongliang Liu

diffusion fine-tuning safety

arXiv AI

Jul 16

Anatomically Faithful but Temporally Blind: Auditing Attribution for Left-Ventricular Ejection-Fraction Estimation from Echocardiography

arXiv:2607. 13738v1 Announce Type: cross Abstract: Background and Objective: Deep video models estimate left-ventricular ejection fraction (EF) from echocardiography with near-expert accuracy, and post-hoc attribution (Chefer relevance for transformers, Grad-CAM for CNNs) is increasingly used to certify that models "look at the right place.

By Hyunkyung Han, Min Jung Kim

llms fine-tuning

arXiv AI

Jul 16

Policy of Thoughts: Scaling Test-Time Training for LLM Reasoning via Online Policy Evolution

arXiv:2601. 20379v2 Announce Type: replace Abstract: Large language models (LLMs) struggle with complex, long-horizon reasoning due to instability caused by their frozen policy assumption.

By Zhengbo Jiao, Hongyu Xian, Qinglong Wang, Yunpu Ma, Zhebo Wang, Zifan Zhang, Dezhang Kong, Meng Han

llms fine-tuning

arXiv AI

Jul 16

LLM-Guided Reinforcement Learning for Audio-Visual Speech Enhancement

arXiv:2603. 13952v3 Announce Type: replace-cross Abstract: In existing Audio-Visual Speech Enhancement (AVSE) methods, objectives such as Scale-Invariant Signal-to-Noise Ratio (SI-SNR) and Mean Squared Error (MSE) are widely used; however, their correlation with perceived speech quality is often suboptimal and provides limited interpretability for optimization.

By Chih-Ning Chen, Jen-Cheng Hou, Hsin-Min Wang, Shao-Yi Chien, Yu Tsao, Fan-Gang Zeng

llms nlp reinforcement-learning fine-tuning multimodal safety

arXiv Machine Learning

Jul 16

Leveraging unlabelled data for generalizable neural population decoding

arXiv:2607. 14086v1 Announce Type: new Abstract: Robust and accurate neural decoders are integral to neurotechnologies such as brain-computer interfaces and closed-loop experiments.

By Ximeng Mao, Nanda H. Krishna, Avery Hee-Woon Ryoo, Matthew G. Perich, Guillaume Lajoie

fine-tuning benchmarks

arXiv AI

Jul 16

Consensus as Privileged Context for Label-Free Self-Distillation

arXiv:2607. 13643v1 Announce Type: cross Abstract: Sampling multiple solutions and returning the majority answer is among the most reliable ways to improve the reasoning accuracy of large language models without labels, and a growing family of methods converts this consensus signal into training supervision.

By John Gkountouras, Josip Juki\'c, Ivan Titov

llms reinforcement-learning fine-tuning efficiency benchmarks

arXiv AI

Jul 16

Data-Efficient Adaptation of LLMs via Attention Head Reweighting

arXiv:2607. 13425v1 Announce Type: cross Abstract: Learning effectively from limited data is critical in domains like security where labeled examples are scarce.

By Tuomas Oikarinen, Zixiao Chen, Charlotte Siska, Tsui-Wei Weng, Chandan Singh, Jianfeng Gao

llms nlp fine-tuning

arXiv Machine Learning

Jul 16

Constraint-Driven Model Optimization: An Industry Framework for Selecting Compression and Acceleration Techniques in Modern Machine Learning Systems

arXiv:2607. 13735v1 Announce Type: new Abstract: The rapid deployment of machine learning systems across cloud, edge, and enterprise environments has brought model optimization to the forefront of systems-engineering.

By Dhruv Shivkant, Saket Mohanty, Utkarsh Wadhwa

fine-tuning efficiency

arXiv AI

Jul 16

ShortOPD: Recovering Pruned LLMs with Short-to-Long On-Policy Distillation

arXiv:2607. 13124v1 Announce Type: cross Abstract: Structured pruning is a hardware-friendly way to compress LLMs, but it is mostly validated on multiple-choice recognition tasks, while the same compressed checkpoints can collapse on the free-form generation that deployment actually requires.

By Qingyu Zhang, Qianhao Yuan, Hongyu Lin, Yaojie Lu, Xianpei Han, Le Sun, Xiang Li, Ming Xu, Jiarui Li, Xiuyin Zhao

llms fine-tuning efficiency benchmarks

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