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 3

Self-Soupervision: Cooking Model Soups without Labels

arXiv:2602. 02890v2 Announce Type: replace Abstract: Model soups are strange and strangely effective combinations of parameters.

By Anthony Fuller, James R. Green, Evan Shelhamer

fine-tuning

arXiv Machine Learning

Jun 3

Visual Instruction Tuning Aligns Modalities through Abstraction

arXiv:2606. 03871v1 Announce Type: cross Abstract: Visual instruction tuning effectively adapts a pre-trained Large Language Model (LLM) to process image information alongside text.

By Luis Palacios, Lorenzo Basile, Diego Doimo, Alberto Cazzaniga

llms rag fine-tuning multimodal benchmarks safety

arXiv AI

Jun 3

Leveraging BART to Assess CS1 C++ Programming Assignments using Rubric-based Criteria

arXiv:2606. 03814v1 Announce Type: new Abstract: This paper investigates rubric-aware, multitask fine-tuning of transformer models for automated grading of introductory C++ programming assignments, with the goal of producing grade predictions that better reflect instructor grading behavior than general-purpose LLMs.

By Kelsey Rainey, Jesse Roberts

llms fine-tuning safety

arXiv Machine Learning

Jun 3

Skill-RM: Unifying Heterogeneous Evaluation Criteria via Agent Skill

arXiv:2606. 03980v1 Announce Type: new Abstract: Reward models (RMs) provide critical feedback signals for LLM post-training, notably in reinforced fine-tuning (RFT) and reinforcement learning (RL) pipelines.

By Tao Chen, Gangwei Jiang, Pengyu Cheng, Siyuan Huang, Yihao Liu, Jingwei Ni, Jiaqi Guo, Mengyu Zhou, Kai Tang, Junling Liu, Qinliang Su, Xiaoxi Jiang, Guanjun Jiang

llms agents reinforcement-learning fine-tuning benchmarks

arXiv Machine Learning

Jun 3

Spatial Transcriptomics-Guided Alignment Enhances Molecular Profiling in Pathology Foundation Model

arXiv:2606. 03644v1 Announce Type: new Abstract: Comprehensive molecular profiling is essential for modern precision oncology but remains hindered by prohibitive costs, specimen exhaustion, and protracted turnaround times.

By Fengtao Zhou, Yingxue Xu, Zhengyu Zhang, Yihui Wang, Zhengrui Guo, Ling Liang, Jiabo Ma, Cheng Jin, Ziyi Liu, Huajun Zhou, Hongyi Wang, Du Cai, Chenglong Zhao, Xi Wang, Can Yang, Yu Wang, Wenbin Li, Feng Gao, Zhe Wang, Zhenhui Li, Xiuming Zhang, Li Liang, Hao Chen

fine-tuning multimodal safety

arXiv Machine Learning

Jun 3

Synthetic Hallucinations, Real Gains: Hard Negatives from Frontier Models for FIM Hallucination Mitigation

arXiv:2606. 03130v1 Announce Type: new Abstract: Small open-source code models that power IDE autocomplete still emit hallucinated Fill-in-the-Middle (FIM) completions: syntactically natural calls to methods, parameters, variables, and imports that do not exist in the surrounding project.

By Mahdi Erfanian, Nelson Daniel Troncoso, Aashna Garg, Amabel Gale, Xiaoyu Liu, Pareesa Ameneh Golnari, Shengyu Fu

llms reinforcement-learning fine-tuning benchmarks safety

arXiv Machine Learning

Jun 3

RESCAST-100K: A Comprehensive Dataset for Cross-Domain Residential Load and Indoor Temperature Forecasting

arXiv:2606. 02852v1 Announce Type: new Abstract: Accurate short-term forecasting of residential energy load and indoor temperature is essential for home energy management systems, grid-level demand response, and community energy efficiency efforts.

By Jainam Dhruva, Yousaf Raza, A. B. Siddique, Simone Silvestri

llms robotics fine-tuning benchmarks

arXiv Machine Learning

Jun 3

Pruning Deep Neural Networks via the Marchenko--Pastur Distribution

arXiv:2606. 02608v1 Announce Type: new Abstract: We study a Marchenko--Pastur (MP) random-matrix approach to pruning deep neural networks with very small post-pruning fine-tuning budgets.

By Leonid Berlyand, Theo Bourdais, Houman Owhad, Yitzchak Shmalo

fine-tuning efficiency

arXiv AI

Jun 3

Narrow Secret Loyalty Dodges Black-Box Audits

arXiv:2605. 06846v3 Announce Type: replace-cross Abstract: Recent work identifies secret loyalties as a distinct threat from standard backdoors.

By Alfie Lamerton, Fabien Roger

fine-tuning

arXiv AI

Jun 3

ProEval: Proactive Failure Discovery and Efficient Performance Estimation for Generative AI Evaluation

arXiv:2604. 23099v2 Announce Type: replace-cross Abstract: Evaluating generative AI models is increasingly resource-intensive due to slow inference, expensive raters, and a rapidly growing landscape of models and benchmarks.

By Yizheng Huang, Wenjun Zeng, Aditi Kumaresan, Zi Wang

fine-tuning benchmarks safety

arXiv AI

Jun 3

PHASE: Physiology-Aware Hyperspectral Reconstruction via Object-to-Human Domain Adaptation

arXiv:2511. 13020v2 Announce Type: replace-cross Abstract: Although hyperspectral imaging offers unparalleled non-invasive physiological insight, its bulky hardware, slow acquisition, and regulatory burden severely limit its clinical availability.

By Yufei Wen, Shuxing Zhong, Jingdan Kang, Yuting Zhang, Jintai Chen, Kaishun Wu

fine-tuning benchmarks safety

arXiv AI

Jun 3

Learning the Neighborhood: Contrast-Free Multimodal Self-Supervised Molecular Graph Pretraining

arXiv:2509. 22468v2 Announce Type: replace-cross Abstract: High-quality molecular representations are essential for property prediction and molecular design, yet large labeled datasets remain scarce.

By Boshra Ariguib, Mathias Niepert, Andrei Manolache

llms rag diffusion fine-tuning multimodal benchmarks

arXiv AI

Jun 3

CoMPAS3D: A Dataset and Benchmark for Interactive Motion

arXiv:2507. 19684v2 Announce Type: replace-cross Abstract: Socially interactive humanoid robots must engage with humans through their bodies, adapting in real time to a partner's movement, intent, and abilities.

By Bermet Burkanova, Yasaman Etesam, Payam Jome Yazdian, Trinity Evans, Chuxuan Zhang, Zoe Stanley, Paige Tutt\"os\'i, Angelica Lim

llms robotics fine-tuning multimodal benchmarks safety

arXiv AI

Jun 3

Typhoon: Towards an Effective Task-Specific Masking Strategy for Pre-trained Language Models

arXiv:2303. 15619v2 Announce Type: replace-cross Abstract: The choice of \emph{which} tokens to mask is a central, under-examined design decision in masked language modeling (MLM).

By Muhammed Shahir Abdurrahman, Hashem Elezabi, Bruce Changlong Xu

llms fine-tuning

arXiv AI

Jun 3

PEAM: Parametric Embodied Agent Memory through Contrastive Internalization of Experience in Minecraft

arXiv:2605. 27762v2 Announce Type: replace Abstract: We present PEAM, a Parametric Embodied Agent Memory framework in Minecraft that transforms agent memory from inference-time retrieval into parameter-resident skills internalized through experience.

By Yuchen Guo, Junli Gong, Weicheng Wang, Hongmin Cai, Yiu-ming Cheung, Weifeng Su

llms agents robotics fine-tuning multimodal

arXiv AI

Jun 3

Human-Like Goalkeeping in a Realistic Football Simulation: a Sample-Efficient Reinforcement Learning Approach

arXiv:2510. 23216v4 Announce Type: replace Abstract: While several high profile video games have served as testbeds for Deep Reinforcement Learning (DRL), this technique has rarely been employed by the game industry for crafting authentic AI behaviors.

By Alessandro Sestini, Joakim Bergdahl, Jean-Philippe Barrette-LaPierre, Florian Fuchs, Brady Chen, Fabio Zinno, Michael Jones, Linus Gissl\'en

agents reinforcement-learning fine-tuning benchmarks

arXiv AI

Jun 3

QUBRIC: Co-Designing Queries and Rubrics for RL Beyond Verifiable Rewards

arXiv:2606. 03968v1 Announce Type: cross Abstract: Rubric-based RL is a promising route for extending reinforcement learning beyond verifiable rewards, yet existing methods optimize rubrics while treating the query distribution as fixed.

By Rongzhi Zhang, Rui Feng, Zhihan Zhang, Jingfeng Yang, Qingyu Yin, Xin Liu, Zixuan Zhang, Priyanka Nigam, Bing Yin, Tuo Zhao, Chao Zhang

reinforcement-learning fine-tuning benchmarks

arXiv AI

Jun 3

Taiji: Pareto Optimal Policy Optimization with Semantics-IDs Trade-off for Industrial LLM-Enhanced Recommendation

arXiv:2606. 03866v1 Announce Type: cross Abstract: Scaling recommender systems via large language models (LLMs) has become a prominent trend in the industry.

By Yuecheng Li, Zeyu Song, Jing Yao, Chi Lu, Peng Jiang, Kun Gai

llms reinforcement-learning fine-tuning safety

arXiv AI

Jun 3

ReLoRA: Knowledge-Reusing Adaptation for Fast Rollout of Evolving LLM Services

arXiv:2606. 02606v1 Announce Type: cross Abstract: Large Language Models (LLMs) are increasingly deployed as continuously evolving services, where frequent base-model updates may invalidate previously deployed task-specific Low-Rank Adaptation (LoRA) adapters.

By Yang Xu, Zihuai Xu, Hongli Xu, Yunming Liao, Zhiwei Yao, Xitong Fu

llms fine-tuning

arXiv AI

Jun 3

CRAM-ER: Error-Resilient Spintronic Computational Random Access Memory for Scalable In-Memory Computation

arXiv:2606. 02781v1 Announce Type: cross Abstract: Deep neural networks (DNNs) have achieved state-of-the-art performance across diverse domains.

By Sohan Salahuddin Mugdho, Md. Shahedul Hasan, Brahmdutta Dixit, Yang Lv, Jian-Ping Wang, Cheng Wang

fine-tuning efficiency benchmarks

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