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

GRASP: Plan-Guided Graph Retrieval with Adaptive Fusion and Reranking on Semi-Structured Knowledge Bases

arXiv:2605. 30237v2 Announce Type: replace-cross Abstract: Semi-structured knowledge bases (SKBs) embed textual documents in a typed graph of entities and relations, and underpin applications such as product search, academic paper search, and precision-medicine inquiries.

By Yicheng Tao, Yiqun Wang, Xiangchen Song, Xin Luo, Kai Liu, Jie Liu

rag fine-tuning benchmarks

arXiv Machine Learning

Jun 2

OncoReason: Structuring Clinical Reasoning in LLMs for Robust and Interpretable Survival Prediction

arXiv:2510. 17532v2 Announce Type: replace-cross Abstract: Predicting cancer treatment outcomes requires models that are both accurate and interpretable, particularly in the presence of heterogeneous clinical data.

By Raghu Vamshi Hemadri, Geetha Krishna Guruju, Kristi Topollai, Anna Ewa Choromanska

llms nlp reinforcement-learning fine-tuning benchmarks safety

arXiv AI

Jun 2

GiPL: Generative augmented iterative Pseudo-Labeling for Cross-Domain Few-Shot Object Detection

arXiv:2605. 29539v2 Announce Type: replace-cross Abstract: Vision-language foundation models have shown promising zero-shot generalization for Cross-Domain Few-Shot Object Detection (CD-FSOD).

By Jiacong Liu, Shu Luo, Yikai Qin, Yaze Zhao, Yongwei Jiang, Yixiong Zou

llms computer-vision fine-tuning multimodal benchmarks

arXiv AI

Jun 2

DSA-Tokenizer: Disentangled Semantic-Acoustic Tokenization via Flow Matching-based Hierarchical Fusion

arXiv:2601. 09239v5 Announce Type: replace-cross Abstract: Speech tokenizers are a key building block of fully discrete Speech LLMs.

By Hanlin Zhang, Daxin Tan, Dehua Tao, Xiao Chen, Haochen Tan, Yunhe Li, Yuchen Cao, Linqi Song

llms diffusion nlp fine-tuning multimodal

arXiv AI

Jun 2

Hallucination Detection-Guided Preference Optimization for Clinical Summarization

arXiv:2605. 28910v2 Announce Type: replace-cross Abstract: Large language models (LLMs) have shown promise on summarization tasks, but they often produce hallucinations, which are unsupported or incorrect statements that limit their reliability in specialized healthcare applications.

By Shamanth Kuthpadi Seethakantha, Dung Ngoc Thai, Vara Prasad Gudi, Simran Tiwari, Rami Matar, Avijit Mitra, Wenlong Zhao, Andrew McCallum, Wael Salloum

llms nlp fine-tuning safety

arXiv Machine Learning

Jun 2

On the Scaling of PEFT: Towards Million Personal Models of Trillion Parameters

arXiv:2606. 02437v1 Announce Type: new Abstract: Parameter-efficient fine-tuning (PEFT) is usually treated as a cheaper alternative to full fine-tuning.

By Mind Lab, :, Song Cao, Vic Cao, Kaijie Chen, Bunny Fan, Hera Feng, Huan Feng, Arthur Fu, Jun Gao, Hongquan Gu, Aaron Guan, Mutian Hong, Hailee Hou, Peixuan Hua, Charles Huang, Miles Jiang, Nora Jiang, Yuyi Jiang, Autumn Jin, Fancy Kong, Kyrie Lei, Alexy Li, Dawn Li, Ray Li, Theo Li, Wenhao Li, Jiayi Lin, Domini Liu, Heshan Liu, Kairus Liu, Logan Liu, Maeve Luo, Runism Lv, Pony Ma, Verity Niu, Anson Qiu, Vincent Wang, Maxwell Yao, Regis Ye, Wenlin Ye, Yanying Ye, Josh Ying, Danney Zeng, Salmon Zhan, Anya Zhang, Ruijia Zhang, Shiyang Zhang, Sueky Zhang, Ya Zhang, Wei Zhao, Ada Zhou, Sizer Zhou, Xinyue Zhu, Murphy Zhuang

fine-tuning

arXiv AI

Jun 2

RoleCDE:Benchmarking and Mitigating Role-Alignment Trade-offs in Role-Playing Agents

arXiv:2606. 01552v1 Announce Type: new Abstract: Role-playing agents(RPAs) are widely used to steer large language models(LLMs) toward role-consistent behavior, yet existing benchmarks mainly evaluate surface-level fidelity and offer limited insight into decision making under role-alignment value conflicts.

By Huayi Lai, Shichao Song, Simin Niu, Hanyu Wang, Jiawei Yang, Zhouxing Wang, Zhiqiang Yin, Xun Liang

llms agents fine-tuning benchmarks safety

arXiv Machine Learning

Jun 2

Parameter-efficient Dual-encoder Architecture with Differentiable Choquet Integral Fusion for Underwater Acoustic Classification

arXiv:2606. 02341v1 Announce Type: cross Abstract: Underwater acoustic classification has a wide array of oceanic applications, but faces challenges due to an increasingly complex acoustic environment.

By Amirmohammad Mohammadi, Joshua Peeples, Alexandra Van Dine

fine-tuning safety

arXiv Machine Learning

Jun 2

LithoGRPO: Fast Inverse Lithography via GRPO Reinforced Flow Matching

arXiv:2606. 00228v1 Announce Type: new Abstract: In semiconductor manufacturing, lithography projects circuit layouts onto silicon wafers through an optical mask.

By Yao Lai, Xuyuan Xiong, Zeyue Xue, Guojin Chen, Jing Wang, Xihui Liu, Rui Zhang, Robert Mullins, Bei Yu, Ping Luo

diffusion reinforcement-learning fine-tuning benchmarks

arXiv AI

Jun 2

DLLM-JEPA: Joint Embedding Predictive Architectures for Masked Diffusion Language Models

arXiv:2606. 00091v1 Announce Type: cross Abstract: Joint Embedding Predictive Architectures (JEPAs) have reshaped self-supervised representation learning in vision.

By Sangdae Nam

llms rag diffusion fine-tuning benchmarks

arXiv AI

Jun 2

Agent Tools Orchestration Leaks More: Dataset, Benchmark, and Mitigation

arXiv:2512. 16310v3 Announce Type: replace-cross Abstract: LLM-based agents increasingly use multiple external tools to complete complex tasks.

By Yuxuan Qiao, Dongqin Liu, Hongchang Yang, Wei Zhou, Songlin Hu

llms agents reinforcement-learning fine-tuning benchmarks safety

arXiv Machine Learning

Jun 2

ProjQ: Project-and-Quantize for Adapter-Aware LLM Compression

arXiv:2606. 00494v1 Announce Type: new Abstract: Post-Training Quantization (PTQ) and Low-Rank Adaptation (LoRA) constitute the standard pipeline for efficient Large Language Model (LLM) deployment.

By Wneya Yu, Chao Zhang, Li Wang, Samson Lasaulce, Merouane Debbah

llms fine-tuning efficiency

arXiv Machine Learning

Jun 2

Machine Learning for Coding Retail Product Names to Consumer-Price Categories: A Rule-plus-Bag-of-Words Pipeline with Reliability-Weighted Human-in-the-Loop Labeling

arXiv:2606. 02004v1 Announce Type: cross Abstract: Consumer-price measurement increasingly draws on alternative data sources -- scanner, web-scraped, and transaction/receipt data.

By Vladimir Beskorovainyi

nlp fine-tuning

arXiv Machine Learning

Jun 2

Enhancing LLM Metacognition via Cognitive Pairwise Training

arXiv:2606. 00869v1 Announce Type: new Abstract: Reinforcement learning with verifiable rewards (RLVR) has become central to LLM reasoning, but its outcome-level rewards can make models more willing to give confident answers when evidence or reasoning is unreliable.

By Weitao Li, Hao Zhou, Xuanyu Lei, Fandong Meng, Yuanhang Liu, Jingyi Ren, Ante Wang, Xiaolong Wang, Yuanchi Zhang, Fuwen Luo, Guangwen Yang, Lin Gan, Weizhi Ma, Yang Liu

llms reinforcement-learning fine-tuning safety

arXiv AI

Jun 2

EvoBrain: Continual Learning of EEG Foundation Models Across Heterogeneous BCI Tasks

arXiv:2606. 01767v1 Announce Type: new Abstract: Electroencephalography (EEG) is the cornerstone of non-invasive brain-computer interfaces (BCIs), yet conventional decoding relies on fragmented, task-specific architectures that severely limit cross-task scalability.

By Yangxuan Zhou, Sha Zhao, Jiquan Wang, Shijian Li, Gang Pan

fine-tuning efficiency benchmarks

arXiv Machine Learning

Jun 2

OP-LoRA: The Blessing of Dimensionality

arXiv:2412. 10362v2 Announce Type: replace Abstract: Low-rank adapters (LoRA) enable finetuning of large models with only a small number of parameters.

By Piotr Teterwak, Kate Saenko, Bryan A. Plummer, Ser-Nam Lim

diffusion fine-tuning

arXiv Machine Learning

Jun 2

Can Vision Language Models Learn Intuitive Physics from Interaction?

arXiv:2602. 06033v2 Announce Type: replace Abstract: Pre-trained vision language models do not have good intuitions about the physical world.

By Luca M. Schulze Buschoff, Konstantinos Voudouris, Can Demircan, Eric Schulz

llms reinforcement-learning fine-tuning

arXiv Machine Learning

Jun 2

OmniOPD: Logit-Free On-Policy Distillation via Speculative Verification

arXiv:2606. 01476v1 Announce Type: new Abstract: On-Policy Distillation (OPD) trains a student model on its own generative trajectories under dense token-level feedback from a stronger teacher, mitigating both the off-policy distribution shift of Supervised Fine-Tuning (SFT) and the sparse credit assignment of Reinforcement Learning (RL).

By Yuhang Zhou, Lizhu Zhang, Yifan Wu, Mingyi Wang, Peng Bo, Jiayi Liu, Xiangjun Fan, Zhuokai Zhao

llms reinforcement-learning fine-tuning efficiency benchmarks

arXiv Machine Learning

Jun 2

Representation Signatures and Risk-Feedback Alignment in LLM Trading Agents

arXiv:2605. 28850v2 Announce Type: replace Abstract: We study behavioral alignment and representation dynamics of large language model (LLM) agents in financial decision environments.

By Weicheng Xue

llms rag agents fine-tuning safety

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

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