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 9

PACT: Learning Diverse Diagnostic Strategies via Privileged Synthesis and Branch Consensus

arXiv:2606. 08938v1 Announce Type: cross Abstract: Clinical diagnosis requires flexible use of multiple reasoning paradigms under incomplete patient information.

By Gen Li, Yuanze Hu, Zhichao Yang, Qingchen Yu, Jianwei Lv, Yue Guo, Yujing Liu, Faguo Wu, Hongwei Zheng, Xiandong Li, Bo Yuan, Yifan Sun, Zhaoxin Fan

llms agents fine-tuning benchmarks

arXiv AI

Jun 9

Minibatch Selection via Partition Matroid Constrained Gradient Matching

arXiv:2606. 07954v1 Announce Type: cross Abstract: Training large language models (LLMs) on heterogeneous data requires selecting minibatches that balance convergence speed with coverage across domains.

By Prayas Agrawal, Prateek Chanda, Ishita Khatri, Ganesh Ramakrishnan, Bamdev Mishra, Pratik Jawanpuria

llms fine-tuning benchmarks

arXiv AI

Jun 9

Post-training is (Massive) Supervised Learning

arXiv:2606. 07527v1 Announce Type: cross Abstract: The prevailing paradigm for training LLMs has evolved to rely on a massive post-training phase consisting of SFT and RL.

By Michael Hassid, Yossi Adi, Roy Schwartz

llms reinforcement-learning fine-tuning benchmarks

arXiv Machine Learning

Jun 9

Transfer learning for causal forest

arXiv:2606. 07693v1 Announce Type: cross Abstract: Transfer learning addresses the challenge of transfering knowledge from one domain to another.

By B\'er\'enice-Alexia Jocteur (ICJ, PSPM), V\'eronique Maume-Deschamps (ICJ, PSPM), Pierre Ribereau (PSPM, ICJ)

fine-tuning

arXiv AI

Jun 9

Emergence of Context Characteristics Sensitivity in Large Language Models

arXiv:2606. 09525v1 Announce Type: cross Abstract: During instruction fine-tuning (IFT), large language models (LLMs) learn to follow instructions by using the provided context to answer a query.

By Nadya Yuki Wangsajaya, Haeun Yu, Isabelle Augenstein

llms reinforcement-learning fine-tuning

arXiv AI

Jun 9

Activation Steering Induces Emergent Misalignment: A More Comprehensive Evaluation

arXiv:2606. 08682v1 Announce Type: cross Abstract: Activation steering has emerged as a popular inference-time technique for modulating the behavior of large language models (LLMs).

By Qi Cao, Jian Lou, Meiting Liu, Wenjie Feng, Dan Li, See-Kiong Ng, Anh Tuan Luu

llms fine-tuning safety

arXiv AI

Jun 9

Learning Task Mixtures from Task Affinities: A Probabilistic Graphical Model for Supervised Fine-Tuning

arXiv:2507. 12612v4 Announce Type: replace-cross Abstract: Supervised fine-tuning performance for large language models depends strongly on how training budget is distributed across a heterogeneous set of tasks.

By Prateek Chanda, Saral Sureka, Parth Pratim Chatterjee, Krishnateja Killamsetty, Nikhil Shivakumar Nayak, Ganesh Ramakrishnan

llms fine-tuning

arXiv AI

Jun 9

Quantifying and Optimizing Simplicity via Polynomial Representations

arXiv:2605. 29823v2 Announce Type: replace Abstract: Deep networks often exhibit a preference for "simple" solutions, and such a simplicity bias is widely believed to play a key role in generalization.

By Tianren Zhang, Xiangxin Li, Minghao Xiao, Guanyu Chen, Feng Chen

llms nlp reinforcement-learning fine-tuning multimodal safety

arXiv AI

Jun 9

When Behavioral Safety Evaluation Fails: A Representation-Level Perspective

arXiv:2606. 08044v1 Announce Type: cross Abstract: Large Language Model (LLM) safety has often been evaluated at the behavior level, which provides limited evidence of internal robustness, as these evaluations target outputs rather than representation-level vulnerability under intervention.

By Enyi Jiang, Anders Gj{\o}lbye, Yibo Jacky Zhang, Sanmi Koyejo

llms diffusion fine-tuning benchmarks safety

arXiv AI

Jun 9

NutriMLLM: Multimodal Large Language Models for Dietary Micronutrient Analysis

arXiv:2606. 08948v1 Announce Type: cross Abstract: Comprehensive estimation of dietary micronutrients from food images could improve clinical nutrition care, but training such models requires large multimodal datasets linking diverse foods to complete nutrient profiles.

By Runze Yan, Minxiao Wang, Jiaying Lu, Darren Liu, Xiao Hu, Hanqi Luo

llms diffusion fine-tuning multimodal benchmarks safety

arXiv Machine Learning

Jun 9

A Comparison of SSL-Based Feature Extractors and Back-End Classifiers for Spoofing Detection: A Multi-Corpus Training and Cross-Linguistic Analysis

arXiv:2606. 08669v1 Announce Type: cross Abstract: Voice biometric systems face growing threats from spoofing attacks, yet the evaluation of detection models remains inconsistent across datasets.

By Anh-Tuan Dao, Driss Matrouf, Mickael Rouvier, Nicholas Evans

fine-tuning benchmarks safety

arXiv AI

Jun 9

Lost in the Flow with Code Talkers: Unveiling the Instruction-Tuning Tax of Large Language Models in Code Tasks

arXiv:2606. 08676v1 Announce Type: cross Abstract: AI coding assistants have significantly improved developer productivity by automatically suggesting code that aligns with user intent, and many of these tools are now integrated directly into Integrated Development Environments (IDEs).

By Shi Ying Chang, Chiok Yew Ho, Yichen Li, Yintong Huo

llms fine-tuning

arXiv Machine Learning

Jun 9

From Hazard Functions to Language Space: Cox-Supervised Distillation of Survival Risk into a Large Language Model

arXiv:2606. 08945v1 Announce Type: new Abstract: We investigate whether information about time-to-event risk estimated by a Cox proportional hazards model can be transferred into a generative large language model.

By Nicholas I-Hsien Kuo, Blanca Gallego, Louisa Jorm

llms diffusion fine-tuning efficiency

arXiv Machine Learning

Jun 9

Learning from flowsheets: A generative transformer model for autocompletion of flowsheets

arXiv:2208. 00859v2 Announce Type: replace Abstract: We propose a novel method enabling autocompletion of chemical flowsheets.

By Gabriel Vogel, Lukas Schulze Balhorn, Artur M. Schweidtmann

llms fine-tuning

arXiv AI

Jun 9

RetroReasoner: A Reasoning LLM for Strategic Retrosynthesis Prediction

arXiv:2603. 12666v2 Announce Type: replace-cross Abstract: Retrosynthesis prediction aims to identify reactants that can synthesize a given product molecule.

By Hanbum Ko, Chanhui Lee, Ye Rin Kim, Rodrigo Hormazabal, Sehui Han, Sungbin Lim, Sungwoong Kim

llms reinforcement-learning fine-tuning

arXiv AI

Jun 9

Benchmarking Vision-Language-Action Models on SO-101: Failure and Recovery Analysis

arXiv:2606. 08881v1 Announce Type: cross Abstract: Vision-Language-Action (VLA) models have demonstrated strong generalization in robotic manipulation, yet existing evaluations are primarily conducted in simulation or on expensive robotic platforms, leaving their robustness on affordable real-world robots largely unexplored.

By Yi Yu, Xinchuan Qiu

robotics fine-tuning multimodal benchmarks

arXiv Machine Learning

Jun 9

Now You (Still) See Me: Detecting Evasive Steganographic Payloads in LLMs

arXiv:2606. 09411v1 Announce Type: cross Abstract: Large language models can be fine-tuned to encode prompt-borne secrets into fluent, seemingly benign outputs.

By Charles Westphal, Timothy Douglas, Keivan Navaie, Tiago Pimentel, Fernando E. Rosas

llms fine-tuning benchmarks

arXiv AI

Jun 9

Trait-space Monitoring for Emergent Misalignment During Supervised Finetuning

arXiv:2606. 07631v1 Announce Type: cross Abstract: Emergent misalignment (EM) occurs when narrow finetuning causes a model to behave dangerously outside the finetuning task.

By Huy Nghiem, Sy-Tuyen Ho, Sarah Wiegreffe, Hal Daum\'e III

llms fine-tuning safety

arXiv Machine Learning

Jun 9

ReTabSyn: Realistic Tabular Data Synthesis via Reinforcement Learning

arXiv:2603. 10823v2 Announce Type: replace-cross Abstract: Deep generative models can help with data scarcity and privacy by producing synthetic training data, but they struggle in low-data, imbalanced tabular settings to fully learn the complex data distribution.

By Xiaofeng Lin, Seungbae Kim, Zhuoya Li, Zachary DeSoto, Charles Fleming, Guang Cheng

llms reinforcement-learning fine-tuning benchmarks safety

arXiv AI

Jun 9

A Mechanistic Analysis of Adversarial Fine-tuning of Vision Transformers

arXiv:2606. 07593v1 Announce Type: cross Abstract: The widespread use of image classification models in high-risk, real-world situations necessitates making these models robust to slight disturbances or perturbations, such as blurring or sharpening, in the input images.

By Hannah Gao (Massachusetts Institute of Technology), Isha Agarwal (Massachusetts Institute of Technology), Dylan Hadfield-Menell (Massachusetts Institute of Technology), Rachel Ma (Massachusetts Institute of Technology)

llms computer-vision fine-tuning multimodal safety

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