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

Sep 30

Data-Free Pruning of Self-Attention Layers in LLMs

arXiv:2512.20636v2 Announce Type: replace-cross Abstract: Many self-attention sublayers in large language models (LLMs) can be removed with little to no loss. We attribute this to the Attention Suppr...

By Dhananjay Saikumar, Blesson Varghese

llms fine-tuning efficiency

arXiv Machine Learning

Sep 30

Making Cross-Continental Federated Learning Repeatable with FLIP: a Multi-Application Study

arXiv:2609.36001v1 Announce Type: new Abstract: Federated learning (FL) in healthcare remains challenging, as the overhead of rebuilding governance guarantees for every collaboration stops most proje...

By Rafael Garcia-Dias, Alexandre Triay Bagur, Chayanin Tangwiriyasakul, Virginia Fernandez, Parhom Esmaeili, Piyalitt Ittichaiwong, Yang Li, Lawrence Adams, Wason Buncharoen, Martin Chapman, Benjamaporn Chayanond, Sadthavud Chunrod, Tanawat Fongsri, Kass Gibson, Supat Plungprasertkul, Supawit Tangpanithandee, Kanyakorn Veerakanjana, Vicky Goh, Michela Antonelli, Joe Zhang, Kongkiat Kespechara, Sebastien Ourselin, M. Jorge Cardoso

fine-tuning

arXiv Machine Learning

Sep 30

Koa-action: Fast and Consistent Structured Decision Making with Generative LLMs

arXiv:2609.36115v1 Announce Type: new Abstract: Industry applications often demand low-latency classification, yet current large language model (LLM) approaches remain poorly suited for latency-criti...

By Shenghong Dai, Shiva Kumar Pentyala, Yingchi Liu, Shubham Mehrotra, Suman Banerjee, James Zhu, Bin Bi, Sitaram Asur, Phil Mui

llms fine-tuning multimodal benchmarks

arXiv Machine Learning

Sep 30

On-Policy Parameter Update Direction Underlies Generalization in LLM Post-Training

arXiv:2609.36659v1 Announce Type: new Abstract: The strong generalization performance of on-policy post-training paradigms has motivated studies of their parameter update behaviors. However, these st...

By Shufan Shen, Zhongni Hou, Junshu Sun, Yufei Zhang, Wei Lin, Guojun Yin, Qingming Huang, Shuhui Wang

llms fine-tuning

arXiv Machine Learning

Sep 30

CALIBUDGET: Calibration-Guided Source Allocation for Fixed-Budget Mixed-Reasoning Adaptation

arXiv:2609.36721v1 Announce Type: new Abstract: Fixed-budget adaptation from heterogeneous data sources requires deciding not only how much data to use, but how much exposure each source receives. Si...

By Yupeng Chang, Yuan Wu

llms fine-tuning

arXiv Machine Learning

Sep 30

When Can Prefixes Compile LoRA? Exact Resource-Capped Tests for Frozen Attention

arXiv:2609.36766v1 Announce Type: new Abstract: Can a fixed continuous prefix replace a given low-rank adapter while the attention head stays frozen? In this research, we show that the answer depends...

By Joyanta Jyoti Mondal, Ibne Farabi Shihab

llms fine-tuning

arXiv Machine Learning

Sep 30

Scalable Diffusion SBI for Compositional Inference under Simulator Misspecification

arXiv:2609.36950v1 Announce Type: new Abstract: Simulation-based inference is challenging when many heterogeneous observations must be composed, hierarchical latent structure must be preserved, and t...

By Vincent D. Zaballa, Elliot E. Hui

diffusion fine-tuning benchmarks

arXiv Machine Learning

Sep 30

Volatility-Clustering Adaptation for Financial Time Series

arXiv:2609.37715v1 Announce Type: new Abstract: Time-series foundation models are increasingly adapted to new domains through fine-tuning on target data, under the implicit assumption that more targe...

By Manh Nguyen, Minh Hoang Nguyen, Huu Hiep Nguyen, Van Dai Do, Hung Le

fine-tuning

arXiv Machine Learning

Sep 30

A foundation model for energy and radiation systems built on heterogeneous scientific interfaces

arXiv:2609.38067v1 Announce Type: new Abstract: Scientific foundation models are commonly evaluated after heterogeneous physical problems have already been translated into a compatible gridded, token...

By Samrendra Roy, Tapas Tripura, Yoon Pyo Lee, Souvik Chakraborty, Syed Bahauddin Alam

fine-tuning

arXiv Machine Learning

Sep 30

Losing the name before the box: measuring and repairing what narrow fine-tuning costs a detector outside its deployment vocabulary

arXiv:2609.36426v1 Announce Type: cross Abstract: A detector pretrained on a broad corpus is fine-tuned on a narrow domain, its in-domain accuracy improves, and it ships. We ask what happens meanwhil...

By Trung Minh Bui, Jongsul Moon, YoungOuk Kim, Jung-Hoon Hwang, Dongin Shin

fine-tuning

arXiv Machine Learning

Sep 30

Backdoor Mitigation in Decentralized LLM Fine-Tuning

arXiv:2609.37367v1 Announce Type: cross Abstract: Decentralized large language model (LLM) fine-tuning lets organizations collaboratively train a shared LLM on data they cannot pool, without a centra...

By Sayan Biswas, Jade Garcia Bourr\'ee, Rachid Guerraoui, Maxime Jacovella, Anne-Marie Kermarrec, Sathwika Peechara, Martijn de Vos, Milos Vujasinovic

llms fine-tuning multimodal benchmarks

arXiv Machine Learning

Sep 30

PoE-Fuse: Precision-Weighted Expert Fusion for Bi-Temporal Change Understanding

arXiv:2609.37485v1 Announce Type: cross Abstract: Bi-temporal change understanding, which localizes and characterizes what changed between two satellite images, is central to disaster response and en...

By Haruki Watase, Shunya Nagashima, Takayuki Nishimura

llms reinforcement-learning fine-tuning multimodal

arXiv Machine Learning

Sep 30

BlenDAgger: Blended Shared Control for Interactive Imitation Learning

arXiv:2609.37599v1 Announce Type: cross Abstract: Robot policies are frequently trained from human corrections, yet teleoperating a robot to provide corrections is burdensome, and human demonstrators...

By Cailyn Smith, Geoffrey Sun, Henny Admoni, Zackory Erickson

agents robotics fine-tuning

arXiv Machine Learning

Sep 30

CompOrca: Corpus-Scale Compliance Labelling of Instruction-Tuning Data

arXiv:2609.37807v1 Announce Type: cross Abstract: Studying how fine-tuning shapes refusal and noncompliance behaviour requires identifying training examples that refuse, evade or otherwise fail to fu...

By Philipp E. Glass, Alina Miron

llms fine-tuning

arXiv Machine Learning

Sep 30

SYNCR: Diagnosing and Learning Cross-Video Reasoning from Simulation

arXiv:2609.37918v1 Announce Type: cross Abstract: Reasoning across videos requires aligning events, matching identities, comparing motion, and integrating partial observations. Evaluating these capab...

By Sara Ghazanfari, Siddharth Garg, Prashanth Krishnamurthy, Farshad Khorrami

llms fine-tuning multimodal benchmarks

arXiv AI

Sep 30

Diffusion Policy Improvement with Proposal-Conditioned Refinement Flows

arXiv:2609.36812v1 Announce Type: cross Abstract: Diffusion and flow policies can model complex behaviors in offline reinforcement learning (RL). However, penalizing their KL divergence from the beha...

By Junhyun Ha, Juho Lee, Byungwoo Park

diffusion reinforcement-learning fine-tuning

arXiv Machine Learning

Sep 30

OpenTumorBoard: A Real-World Benchmark of Multidisciplinary Tumor Board Discussion Trajectories

arXiv:2609.32810v2 Announce Type: replace-cross Abstract: Multidisciplinary tumor boards integrate multimodal clinical observations and longitudinal patient histories through specialist discussions,...

By Anqi Li, Zhixuan Ge, Yixuan Duan, Jiarong Qian, Chi-Yu Chen, MingYu Lu, Huan-Yu Hsu, Yu Gu, Yue Guo, Sheng Wang, Wei Qiu, Hanwen Xu

llms reinforcement-learning fine-tuning multimodal benchmarks safety

arXiv Computation and Language

Sep 30

Learning from Teacher Continuations at Student States

arXiv:2609.36246v1 Announce Type: new Abstract: We present OLIVE (OnLine InterVEntion). At each iteration, the evolving student policy generates a new prefix, the teacher continues it autoregressivel...

By Haojin Wang, Dylan Zhang, Huaibo Chen, Suhao Yu, Yihang Sun, Zhanyang Jin, Jiaying Ye, Dianqi Li, Prasanna Sattigeri, Kamal Youcef-Toumi, Hao Peng

llms agents fine-tuning efficiency

arXiv Computation and Language

Sep 30

Video2Skill: From Streaming Experience to Reusable Embodied Skills

arXiv:2609.36691v1 Announce Type: new Abstract: Manipulation behaviors vary widely across objects and scenes, but they share a small set of reusable skills, and planning with these skills helps embod...

By Jianshu Zhang, Ce Zhang, Xiyuan Yang, Chenwei Xu, Haoran Lu, Yijiang Li, Yaqi Xie, Katia P. Sycara, Han Liu

llms agents robotics fine-tuning multimodal benchmarks

arXiv Computation and Language

Sep 30

Can Language Models Learn to Forecast Stock Prices

arXiv:2609.36914v1 Announce Type: new Abstract: Post-training has been shown to significantly improve language models' performance on tasks with verifiable outcomes, including mathematical reasoning,...

By Jiacheng Guo, Suozhi Huang, Shuzhen Li, Yunlong Gao, Zerui Cheng, Jason Ge, Shushu Liang, Zihao Li, Hao Lu, Ming Yin, Shilong Liu, Jiashuo Liu, Xu Kuang, Mengdi Wang

llms agents reinforcement-learning fine-tuning benchmarks

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