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

Jun 4

Generalizable Multi-Task Learning for Wireless Networks Using Prompt Decision Transformers

arXiv:2606. 04328v1 Announce Type: cross Abstract: Future wireless networks demand rapid adaptation to highly heterogeneous environments and dynamic task configurations, necessitating a shift from conventional rule-based and optimization-driven radio resource management (RRM) toward artificial intelligence (AI)-driven RRM.

By Fatih Temiz, Shavbo Salehi, Melike Erol-Kantarci

llms agents reinforcement-learning fine-tuning

arXiv AI

Jun 4

EvoPrompt: Guided Prompt Evolution for Vision-Language Models Adaptation

arXiv:2603. 09493v2 Announce Type: replace-cross Abstract: The adaptation of large-scale vision-language models (VLMs) to downstream tasks with limited labeled data remains a significant challenge.

By Enming Zhang, Jiayang Li, Yanlong Wang, Yanru Wu, Zhenyu Liu, Yang Li

llms rag fine-tuning multimodal benchmarks

arXiv AI

Jun 4

ZeroWBC: Learning Natural Whole-Body Humanoid Interaction from Human Egocentric Data

arXiv:2603. 09170v2 Announce Type: replace-cross Abstract: Achieving versatile and natural whole-body humanoid interaction control remains challenging due to the high cost of whole-body teleoperation data.

By Haoran Yang, Jiacheng Bao, Yucheng Xin, Haoming Song, Yuyang Tian, Bin Zhao, Dong Wang, Xuelong Li

llms robotics fine-tuning multimodal safety

arXiv Machine Learning

Jun 4

Towards Accurate Model Selection in Deep Unsupervised Domain Adaptation

arXiv:2606. 04665v1 Announce Type: new Abstract: Deep unsupervised domain adaptation (Deep UDA) methods successfully leverage rich labeled data in a source domain to boost the performance on related but unlabeled data in a target domain.

By Kaichao You, Ximei Wang, Mingsheng Long, Michael I. Jordan

fine-tuning

arXiv Machine Learning

Jun 4

Reasoning Shift: How Context Silently Shortens LLM Reasoning

arXiv:2604. 01161v2 Announce Type: replace Abstract: Large language models (LLMs) exhibiting test-time scaling behavior, such as extended reasoning traces and self-verification, have demonstrated remarkable performance on complex, long-term reasoning tasks.

By Gleb Rodionov, Roman Garipov, George Yakushev

llms agents fine-tuning

arXiv Machine Learning

Jun 4

Sequential Data Poisoning in LLM Post-Training

arXiv:2606. 04929v1 Announce Type: new Abstract: LLM post-training proceeds through multiple stages, e.

By Jack Sanderson, Yihan Wang, Xiaoqian Lu, Gautam Kamath, Yiwei Lu

llms reinforcement-learning fine-tuning

arXiv Machine Learning

Jun 4

TANDEM: Bi-Level Data Mixture Optimization with Twin Networks

arXiv:2606. 04401v1 Announce Type: new Abstract: The capabilities of large language models (LLMs) significantly depend on training data drawn from various domains.

By Jiaxing Wang, Deping Xiang, Jin Xu, Mingyang Yi, Guoqiang Gong, Zicheng Zhang, Haoran Li, Pengzhang Liu, Zhen Chen, Ke Zhang, Ju Fan, Qixiang Jiang

llms fine-tuning

arXiv AI

Jun 4

Can VLMs Predict Future States? Bootstrapping World Models from Inverse Dynamics

arXiv:2506. 06006v3 Announce Type: replace-cross Abstract: Can unified vision-language models (VLMs) perform forward dynamics prediction (FDP), i.

By Yifu Qiu, Yftah Ziser, Anna Korhonen, Shay B. Cohen, Edoardo M. Ponti

llms fine-tuning multimodal benchmarks

arXiv AI

Jun 4

Geometry-Aware Hallucination Detection in Large Language Models

arXiv:2601. 06196v3 Announce Type: replace-cross Abstract: Large language models (LLMs) frequently generate factually incorrect or unsupported content, commonly referred to as hallucinations.

By Bodla Krishna Vamshi, Rohan Bhatnagar, Haizhao Yang

llms rag nlp fine-tuning benchmarks safety

arXiv AI

Jun 4

The Differentiable Auditory Loop (DAL): An ML Framework for Hyper-Personalized Hearing Aids

arXiv:2606. 04103v1 Announce Type: cross Abstract: Conventional hearing aids rely on fixed, frequency-dependent amplification and compression to manage reduced sensitivity, which often fails to provide sufficient listening support in complex environments, such as situations with multiple speakers (the ``cocktail party'' problem).

By Alejandro Ballesta Rosen, Jason Mikiel-Hunter, Julian Maclaren, Jack Collins, Richard F. Lyon, Simon Carlile

fine-tuning

arXiv AI

Jun 4

A Unified Geometric Space for Topological Alignment Between Transformer-Based Models and Human Brain Networks

arXiv:2510. 24342v2 Announce Type: replace Abstract: Prior brain-AI alignment studies are typically constrained by specific inputs and tasks, limiting their ability to capture organizational properties across models with different modalities.

By Silin Chen, Yuzhong Chen, Caiwei Wang, Zifan Wang, Junhao Wang, Zifeng Jia, Keith M Kendrick, Tuo Zhang, Lin Zhao, Dezhong Yao, Tianming Liu, Xi Jiang

llms fine-tuning multimodal safety

arXiv Machine Learning

Jun 4

Stateful Visual Encoders for Vision-Language Models

arXiv:2606. 04433v1 Announce Type: cross Abstract: Vision-language models (VLMs) are increasingly used in multi-image, multi-turn agentic settings where decisions depend on visual changes.

By Zirui Wang, Junwei Yu, Adam Yala, David M. Chan, Joseph E. Gonzalez, Trevor Darrell

llms agents fine-tuning multimodal

arXiv AI

Jun 4

Efficiently Aligning Language Models with Online Natural Language Feedback

arXiv:2605. 04356v2 Announce Type: replace-cross Abstract: Reinforcement learning with verifiable rewards has been used to elicit impressive performance from language models in many domains.

By Christine Ye, Joe Benton

llms reinforcement-learning fine-tuning safety

arXiv Machine Learning

Jun 4

Few Tokens, Big Leverage: Preserving Safety Alignment by Constraining Safety Tokens during Fine-tuning

arXiv:2603. 07445v2 Announce Type: replace-cross Abstract: Large language models (LLMs) often require fine-tuning (FT) to perform well on downstream tasks, but FT can induce safety-alignment drift even when the training dataset contains only benign data.

By Guoli Wang, Haonan Shi, Tu Ouyang, An Wang

llms fine-tuning safety

arXiv AI

Jun 4

MIRAGE: Mobile Agents with Implicit Reasoning and Generative World Models

arXiv:2606. 04627v1 Announce Type: new Abstract: Mobile agents are increasingly expected to operate everyday applications from screenshots and language goals, where reliable control requires reasoning over screen affordances, multi-step navigation, and future state changes.

By Zhichao Yang, Yuanze Hu, Haojie Hao, Longkun Hao, Dongshuo Huang, Hongyu Lin, Gen Li, Lanqing Hong, Yihang Lou, Yan Bai

agents diffusion fine-tuning benchmarks

arXiv AI

Jun 4

BiasGRPO: Stabilizing Bias Mitigation in High-Variance Reward Landscapes via Group-Relative Policy Optimization

arXiv:2606. 04807v1 Announce Type: new Abstract: Mitigating social bias in Large Language Models (LLMs) presents a distinct alignment challenge: unlike verifiable tasks, bias lacks a single ground truth, creating a high-variance, subjective reward landscape.

By Saket Reddy, Ke Yang, ChengXiang Zhai

llms reinforcement-learning fine-tuning benchmarks safety

arXiv AI

Jun 4

Overview of the EReL@MIR 2025 Multimodal Document Retrieval Challenge (Track 1)

arXiv:2606. 04240v1 Announce Type: cross Abstract: Retrieval over visually-rich documents, pages that interleave text with figures, tables, and charts, is essential for multimodal retrieval-augmented generation, yet most retrievers still discard the visual channel.

By Jingbiao Mei

llms rag fine-tuning multimodal

arXiv Machine Learning

Jun 4

RL Excursions during Pre-Training: Re-examining Policy Optimization for LLM training

arXiv:2606. 04272v1 Announce Type: new Abstract: The standard LLM training pipeline applies reinforcement learning (RL) only after pre-training and supervised fine-tuning (SFT).

By Rachit Bansal, Clara Mohri, Tian Qin, David Alvarez-Melis, Sham Kakade

llms reinforcement-learning fine-tuning

arXiv Machine Learning

Jun 4

Efficient Reasoning on the Edge

arXiv:2603. 16867v2 Announce Type: replace Abstract: Large language models (LLMs) with chain-of-thought reasoning achieve state-of-the-art performance across complex problem-solving tasks, but their verbose reasoning traces and large context requirements make them impractical for edge deployment.

By Yelysei Bondarenko, Thomas Hehn, Rob Hesselink, Romain Lepert, Fabio Valerio Massoli, Evgeny Mironov, Leyla Mirvakhabova, Tribhuvanesh Orekondy, Spyridon Stasis, Andrey Kuzmin, Anna Kuzina, Markus Nagel, Ankita Nayak, Corrado Rainone, Ork de Rooij, Paul N Whatmough, Arash Behboodi, Babak Ehteshami Bejnordi

llms reinforcement-learning fine-tuning efficiency benchmarks

arXiv AI

Jun 4

Mid-Think: Training-Free Intermediate-Budget Reasoning via Token-Level Triggers

arXiv:2601. 07036v2 Announce Type: replace-cross Abstract: Hybrid reasoning language models are commonly controlled through high-level Think/No-think instructions to regulate reasoning behavior, yet we found that such mode switching is largely driven by a small set of trigger tokens rather than the instructions themselves.

By Wang Yang, Debargha Ganguly, Xinpeng Li, Chaoda Song, Shouren Wang, Vikash Singh, Vipin Chaudhary, Xiaotian Han

llms reinforcement-learning fine-tuning

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