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 17

X-REFINE: XAI-based RElevance input-Filtering and archItecture fiNe-tuning for channel Estimation

arXiv:2602. 22277v2 Announce Type: replace Abstract: AI-native architectures are vital for 6G wireless communications.

By Abdul Karim Gizzini, Yahia Medjahdi

fine-tuning safety

arXiv Machine Learning

Jun 17

ProCUA-SFT Technical Report

arXiv:2606. 17321v1 Announce Type: new Abstract: Training computer-use agents (CUAs) -- models that interact with graphical desktops through screenshots and keyboard/mouse actions -- requires large-scale, diverse trajectory data collected in full desktop environments.

By Jaehun Jung, Ximing Lu, Brandon Cui, Muhammad Khalifa, Shaokun Zhang, Hao Zhang, Jin Xu, Amala Sanjay Deshmukh, Karan Sapra, Andrew Tao, Yejin Choi, Jan Kautz, Mingjie Liu, Yi Dong

agents fine-tuning multimodal

arXiv AI

Jun 17

RLRC: Reinforcement Learning-based Recovery for Compressed Vision-Language-Action Models

arXiv:2506. 17639v2 Announce Type: replace-cross Abstract: Vision-Language-Action models (VLA) have demonstrated remarkable capabilities and strong potential in complex robotic manipulation.

By Yuxuan Chen, Yixin Han, Yize Huang, Xiao Li

reinforcement-learning robotics fine-tuning efficiency multimodal

arXiv Machine Learning

Jun 17

Clarify Before You Draw: Proactive Agents for Robust Text-to-CAD Generation

arXiv:2602. 03045v2 Announce Type: replace Abstract: Large language models have recently enabled text-to-CAD systems that synthesize parametric CAD programs (e.

By Bo Yuan, Zelin Zhao, Petr Molodyk, Bin Hu, Yongxin Chen

llms agents fine-tuning

arXiv AI

Jun 17

Catastrophic Forgetting is Low-Rank: A Function-Space Theory for Continual Adaptation

arXiv:2606. 18024v1 Announce Type: cross Abstract: Catastrophic forgetting in continual adaptation is usually studied through parameter drift, replay, or distillation, but these views do not identify which output-space directions are vulnerable.

By Ido Nitzan Hidekel, Dan Raviv

fine-tuning efficiency

arXiv Machine Learning

Jun 17

From Reasoning Traces to Reusable Modules: Understanding Compositional Generalization in Language Model Reasoning

arXiv:2606. 18089v1 Announce Type: new Abstract: Post-training pipelines that combine supervised fine-tuning (SFT) with reinforcement learning (RL) have emerged as the key recipe for transforming large language models (LLMs) into robust reasoners.

By Lingjing Kong, Xin Liu, Guangyi Chen, Martin Q. Ma, Xiangchen Song, Yuekai Sun, Mikhail Yurochkin, Taylor W. Killian, Ruslan Salakhutdinov, Kun Zhang, Eric P. Xing, Zhengzhong Liu

llms reinforcement-learning fine-tuning

arXiv Machine Learning

Jun 17

From Drift to Coherence: Stabilizing Beliefs in LLMs

arXiv:2606. 17832v1 Announce Type: new Abstract: Large language models (LLMs) are often hypothesized to perform implicit Bayesian inference, yet a key coherence condition, the martingale property of predictive beliefs, has been shown to fail in controlled synthetic in-context learning settings.

By SongEun Kim, Seungyoo Lee, Edwin Fong, Hyungi Lee, Juho Lee

llms nlp fine-tuning benchmarks

arXiv AI

Jun 17

See First, Answer Later: Visual Evidence Pre-Alignment via Sufficiency-Driven RL

arXiv:2606. 17678v1 Announce Type: cross Abstract: Multimodal large language models (MLLMs) integrate strong text reasoning with visual inputs, yet their responses can be inconsistent with the underlying images, indicating ineffective utilization of visual evidence during inference.

By Yilian Liu, Sicong Leng, Guoshun Nan, Junyi Zhu, Jiayu Huang, Minghao Sun, Xuancheng Zhu, Yisong Chen, Zexian Wei, Xiaofeng Tao

llms reinforcement-learning fine-tuning multimodal benchmarks safety

arXiv Machine Learning

Jun 17

Learn-To-Learn on Arbitrary Textual Conditioning: A Hypernetwork-Driven Meta-Gated LLM

arXiv:2605. 01973v3 Announce Type: replace-cross Abstract: Conventional LLMs may suffer from corpus heterogeneity and subtle condition changes.

By Luo Ji, Qi Qin, Ningyuan Xi, Teng Chen, Qingqing Gu, Hongyan Li

llms fine-tuning

arXiv AI

Jun 17

RooseBERT: A New Deal For Political Language Modelling

arXiv:2508. 03250v4 Announce Type: replace-cross Abstract: The increasing amount of political debates and politics-related discussions calls for the definition of novel computational methods to automatically analyse such content with the final goal of lightening up political deliberation to citizens.

By Deborah Dore, Elena Cabrio, Serena Villata

llms nlp fine-tuning

arXiv Machine Learning

Jun 17

ReRAM-aware Model Finetuning addressing I-V Non-linearity and Retention Errors

arXiv:2606. 17471v1 Announce Type: new Abstract: Traditional CPU, GPU, and NPU architectures are increasingly limited by the von Neumann bottleneck.

By Ching-Yi Lin, Shamik Kundu, Arnab Raha, Sahil Shah

computer-vision fine-tuning

arXiv AI

Jun 17

NarrativeWorldBench: A Frontier-Saturated Benchmark and a Latent World Model for Long-Horizon Co-Creative Audio Drama

arXiv:2606. 17391v1 Announce Type: cross Abstract: Long-form serialized audio drama, with arcs that run for 200 to 800 episodes, is a major creative medium and a setting where frontier large language models (LLMs) fail.

By Logan Mann, Abdur Rahman, Mohammad Saifullah, Taaha Kazi, Vasu Sharma

llms fine-tuning benchmarks

arXiv AI

Jun 17

Reinforcing Dual-Path Reasoning in Spatial Vision Language Models

arXiv:2606. 17539v1 Announce Type: cross Abstract: Spatial VLMs have made substantial progress in geometric perception, yet complex spatial reasoning requiring multi-step inference over depth, distance, and scene relations remains challenging.

By Yatai Ji, An-Chieh Cheng, Yang Fu, Yukang Chen, Han Zhang, Zhaojing Yang, Wei Huang, Ka Chun Cheung, Song Han, Vidya Nariyambut Murali, Pavlo Molchanov, Jan Kautz, Simon See, Hongxu Yin, Ping Luo, Sifei Liu

llms reinforcement-learning fine-tuning multimodal benchmarks safety

arXiv AI

Jun 17

SuCo: Sufficiency-guided Continuous Adaptive Reasoning

arXiv:2606. 17687v1 Announce Type: cross Abstract: Despite remarkable performance on complex tasks, Large Reasoning Models (LRMs) often generate excessively long Chain-of-Thoughts (CoT), inflating computational costs even for simple queries.

By Jiahao Wang, Bingyu Liang, Chenhao Hu, Longhui Zhang, Xuebo Liu, Min zhang, Jing Li, Xuelong Li

agents reinforcement-learning fine-tuning benchmarks

arXiv Machine Learning

Jun 17

Multi-Source Cybersecurity Logs: An ATT&CK-Labeled Dataset and SLM Evaluation

arXiv:2606. 18190v1 Announce Type: cross Abstract: Multi-stage cyberattacks span system, network, and browser logs.

By Abir Ashab Niloy, Ahmed Ryan, Imamul Hossain Rafi, Md Erfan, Md Rayhanur Rahman

llms fine-tuning safety

arXiv AI

Jun 17

Any2Any: Efficient Cross-Embodiment Transfer for Humanoid Whole-Body Tracking

arXiv:2605. 23733v2 Announce Type: replace-cross Abstract: Whole-body tracking (WBT) models have become a key foundation for humanoid robots, enabling them to imitate diverse motions with high fidelity.

By Ming Yang, Tao Yu, Feng Li, Hua Chen

robotics fine-tuning safety

arXiv AI

Jun 17

TuneAhead: Predicting Fine-tuning Performance Before Full Training Begins

arXiv:2606. 17660v1 Announce Type: cross Abstract: Fine-tuning large language models (LLMs) is compute-intensive and error-prone: model performance depends sensitively on data quality and hyperparameter choices, and na\"ive runs can even degrade model performance.

By Yuxiang Luo, Haonan Long, Chen Wang, Qiqi Duan, Xiaotian Lin, Yanwei Xu, Yuyu Luo, Weikai Yang, Nan Tang

llms fine-tuning data-engineering

arXiv Machine Learning

Jun 17

Uncertainty Quantification for Flow-Based Vision-Language-Action Models

arXiv:2606. 18043v1 Announce Type: cross Abstract: Vision-language-action models (VLAs) combine vision-language backbones with expressive generative action heads trained via flow matching on large-scale robotic datasets.

By Ralf R\"omer, Maximilian Seeliger, Saida Liu, Ben Sturgis, Marco Bagatella, Daniel Marta, Andreas Krause, Angela P. Schoellig

diffusion robotics fine-tuning multimodal benchmarks

arXiv AI

Jun 17

Rift: A Conflict Signature for Deception in Language Models

arXiv:2606. 17229v1 Announce Type: cross Abstract: A model that lies while knowing the truth is the central case ELK cannot handle with behavioral evaluation alone.

By Petr Nyoma

llms agents fine-tuning safety

arXiv AI

Jun 17

Visual Verification Enables Inference-time Steering and Autonomous Policy Improvement

arXiv:2606. 18247v1 Announce Type: cross Abstract: Robots deployed in the real world should learn from their experience and improve over time.

By Mingtong Zhang, Dhruv Shah

agents robotics fine-tuning

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