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 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

SciDER: Scientific Data-centric End-to-end Researcher

arXiv:2603. 01421v3 Announce Type: replace Abstract: While large language models accelerate scientific discovery, existing agents face severe limitations in adaptability, domain generalization, and multimodal scalability, often struggling to autonomously process raw, domain-specific experimental data.

By Ke Lin, Owais Aijaz, Yilin Lu, Yiyang Luo, Xuehang Guo, Preslav Nakov

llms agents fine-tuning multimodal benchmarks

arXiv AI

Jun 4

Evaluating Zero-Shot and One-Shot Adaptation of Small Language Models in Leader-Follower Interaction

arXiv:2602. 23312v3 Announce Type: replace-cross Abstract: Leader-follower interaction is an important paradigm in human-robot interaction (HRI).

By Rafael R. Baptista, Andr\'e de Lima Salgado, Ricardo V. Godoy, Marcelo Becker, Thiago Boaventura, Gustavo J. G. Lahr

llms robotics fine-tuning benchmarks

arXiv AI

Jun 4

MM-BizRAG: Rethinking Multimodal Retrieval-Augmented Generation for General Purpose Enterprise Q&A

arXiv:2606. 04231v1 Announce Type: cross Abstract: Recent advances in multimodal retrieval-augmented generation (MM-RAG) have shifted toward minimal parsing, relying on page-level images for producing retriever embeddings and for answer generation.

By Hanoz Bhathena, Parin Rajesh Jhaveri, Rohan Mittal, Prateek Singh, Aymen Kallala, Rachneet Kaur, Yiqiao Jin, Zhen Zeng, Adwait Ratnaparkhi, Denis Kochedykov

llms rag fine-tuning multimodal benchmarks safety

arXiv AI

Jun 4

ChatSOP: An SOP-Guided MCTS Planning Framework for Controllable LLM Dialogue Agents

arXiv:2407. 03884v4 Announce Type: replace-cross Abstract: Dialogue agents powered by Large Language Models (LLMs) show superior performance in various tasks.

By Zhigen Li, Jianxiang Peng, Yanmeng Wang, Yong Cao, Tianhao Shen, Minghui Zhang, Linxi Su, Shang Wu, Yihang Wu, Yuqian Wang, Ye Wang, Wei Hu, Jianfeng Li, Shaojun Wang, Jing Xiao, Deyi Xiong

llms agents fine-tuning

arXiv AI

Jun 4

Automatic Generation of Titles for Research Papers Using Language Models

arXiv:2606. 05085v1 Announce Type: cross Abstract: The title of a research paper conveys its primary idea and, occasionally, its conclusions in a clear and concise manner.

By Tohida Rehman, Debarshi Kumar Sanyal, Samiran Chattopadhyay

llms fine-tuning

arXiv AI

Jun 4

Tuning the Implicit Regularizer of Masked Diffusion Language Models: Enhancing Generalization via Insights from $k$-Parity

arXiv:2601. 22450v2 Announce Type: replace-cross Abstract: Masked Diffusion Language Models have recently emerged as a powerful generative paradigm, yet their generalization properties remain understudied compared to their auto-regressive counterparts.

By Jianhao Huang, Baharan Mirzasoleiman

llms diffusion fine-tuning

arXiv Machine Learning

Jun 4

Plug-and-Play Diffusion Meets ADMM: Dual-Variable Coupling for Robust Medical Image Reconstruction

arXiv:2602. 23214v2 Announce Type: replace-cross Abstract: Plug-and-Play diffusion prior (PnPDP) frameworks have emerged as a powerful paradigm for solving imaging inverse problems by treating pretrained generative models as modular priors.

By Chenhe Du, Xuanyu Tian, Qing Wu, Muyu Liu, Jingyi Yu, Hongjiang Wei, Yuyao Zhang

diffusion fine-tuning benchmarks safety

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

Invariant Gradient Alignment for Robust Reasoning Distillation

arXiv:2606. 05025v1 Announce Type: cross Abstract: Large language models (LLMs) suffer from shortcut learning: they systematically fail on out-of-distribution (OOD) inputs whose semantic surface differs from training data, even when the logical structure is identical.

By Zehua Cheng, Wei Dai, Jiahao Sun

llms fine-tuning efficiency benchmarks safety

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

SePO: Self-Evolving Prompt Agent for System Prompt Optimization

arXiv:2606. 04465v1 Announce Type: cross Abstract: System prompt optimization improves agent behavior without modifying the underlying model, yielding human-readable, model-agnostic instructions.

By Wangcheng Tao, Han Wu, Weng-Fai Wong

agents fine-tuning benchmarks

arXiv AI

Jun 4

Test-time reward-guided alignment of language models by importance sampling on pre-logit space

arXiv:2510. 26219v3 Announce Type: replace-cross Abstract: Test-time alignment of large language models (LLMs) attracts attention because fine-tuning of LLMs requires high computational costs.

By Sekitoshi Kanai, Tsukasa Yoshida, Hiroshi Takahashi, Haru Kuroki, Kazumune Hashimoto

llms fine-tuning safety

arXiv Machine Learning

Jun 4

Symbolic Regression for Shared Expressions: Introducing Partial Parameter Sharing

arXiv:2601. 04051v3 Announce Type: replace Abstract: Symbolic regression aims to find symbolic expressions that describe datasets.

By Viktor Martinek, Roland Herzog

fine-tuning safety

arXiv AI

Jun 4

Success Conditioning as Policy Improvement: The Optimization Problem Solved by Imitating Success

arXiv:2601. 18175v2 Announce Type: replace Abstract: A widely used technique for improving policies is success conditioning, in which one collects trajectories, identifies those that achieve a desired outcome, and updates the policy to imitate the actions taken along successful trajectories.

By Daniel Russo

llms reinforcement-learning fine-tuning

arXiv AI

Jun 4

Building The Ph(ysical)AI Layer Of Machine Intelligence

arXiv:2606. 04106v1 Announce Type: cross Abstract: Foundation models achieve generalization through massive-scale training on diverse data, but have limitations with transfer to truly unseen domains without paired training data.

By Ulbert Jose Botero, Liam Smith, Brooks Olney, Pooya Khorrami, Steven Kusiak, Watson Jia, Sage Trudeau, Daniel Capecci

fine-tuning multimodal

arXiv AI

Jun 4

R-APS: Compositional Reasoning and In-Context Meta-Learning for Constrained Design via Reflective Adversarial Pareto Search

arXiv:2606. 04823v1 Announce Type: new Abstract: Large language models (LLMs) are fluent on open-ended tasks, yet in agentic settings, where a system must plan, use tools, and act over extended horizons, fluency does not ensure reliable delivery.

By Jo\~ao Pedro Gandarela, Thiago Rios, Stefan Menzel, Andr\'e Freitas

llms agents robotics fine-tuning safety

arXiv Machine Learning

Jun 4

Testing Neural Networks via Bayesian-Guided Exploration of Decision Landscapes

arXiv:2606. 04314v1 Announce Type: new Abstract: As neural networks are increasingly deployed in safety-critical domains, testing is essential to evaluate and improve their reliability.

By Bin Duan, Meiru Che, Guowei Yang

fine-tuning safety

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 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

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