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 26

Multipath Adaptive Gated Bottleneck Latent ODE with Raman Data Fusion for Cell Culture Process Forecasting

arXiv:2606. 26520v1 Announce Type: cross Abstract: Mammalian cell-culture processes underpin the manufacture of many biopharmaceuticals, yet keeping a run on track is hard: critical process parameters drift over days, and an off-specification trend is often confirmed too late to intervene.

By Johnny Peng, Thanh Tung Khuat, Ellen Otte, Katarzyna Musial, Bogdan Gabrys

fine-tuning

arXiv AI

Jun 26

Small edits, large models: How Wikipedia advocacy shapes LLM values

arXiv:2606. 24890v2 Announce Type: replace-cross Abstract: Can a small group of volunteers shape how AI systems discuss animal welfare, just by editing Wikipedia?

By Jasmine Brazilek, Maria Navas, Alexa Gnauck

llms fine-tuning

arXiv AI

Jun 26

Play2Perfect: What Matters in Dexterous Play Pretraining for Precise Assembly?

arXiv:2606. 26428v1 Announce Type: cross Abstract: Multi-fingered robots promise the speed and dexterity of human hands, yet challenging problems such as precise assembly have remained out of reach.

By Tyler Ga Wei Lum, Kushal Kedia, C. Karen Liu, Jeannette Bohg

reinforcement-learning robotics fine-tuning

arXiv AI

Jun 26

Narration-of-Thought: Inference-Time Scaffolding for Defeasible Ethical Reasoning in Large Language Models

arXiv:2606. 26366v1 Announce Type: new Abstract: Standard chain-of-thought on moral dilemmas exhibits two failure modes: stakeholder collapse (the trace names at most one party with a stake in the outcome) and uncertainty suppression (no explicit unknowns or hedges before committing to an action).

By Patrick Cooper, Alvaro Velasquez

llms agents fine-tuning benchmarks

arXiv Machine Learning

Jun 26

Sample-efficient Transfer Reinforcement Learning via Adaptive Reward Shaping and Policy-Ratio Reweighting Strategy

arXiv:2606. 26527v1 Announce Type: new Abstract: Transfer learning improves policy learning efficiency by reusing knowledge from source tasks, providing a feasible paradigm for safe and efficient autonomous highway lane changing decision-making.

By Wenjie Huang, Yang Li, Jingjia Teng, Mingwei Jin, Kai Song, Yougang Bian, Yongfu Li, Qisong Yang, Helai Huang

agents reinforcement-learning fine-tuning safety

arXiv AI

Jun 26

LiMoDE: Rethinking Lifelong Robot Manipulation from a Mixture-of-Dynamic-Experts Perspective

arXiv:2606. 26183v1 Announce Type: cross Abstract: Building a generalist robot that can leverage prior knowledge for continuous task adaptation remains a significant challenge.

By Zhihao Gu, Lin Wang

robotics fine-tuning benchmarks

arXiv Machine Learning

Jun 26

Just how sure are you? Improving Verbalized Uncertainty Calibration in Medical VQA

arXiv:2606. 27023v1 Announce Type: new Abstract: Multimodal large language models (MLLMs) applied to Medical Visual Question Answering (VQA) tend to produce overconfident outputs regardless of actual correctness, and existing verbalized confidence calibration methods, developed primarily for text only LLMs, do not account for the multimodal nature of medical image understanding.

By Eren Senoglu, Federico Toschi, Nicolo Brunello, Andrea Sassella, Mark James Carman

llms nlp fine-tuning multimodal benchmarks safety

arXiv AI

Jun 26

From Celebrities to Anyone: Characterizing AI Nudification Content, Technology, and Community Dynamics on 4chan

arXiv:2606. 27234v1 Announce Type: cross Abstract: AI nudification uses generative models to create synthetic non-consensual sexually explicit imagery (SNEACI) of real individuals.

By Chi Cui, Yixin Wu, Yang Zhang

diffusion fine-tuning

arXiv Machine Learning

Jun 26

GRAG: Generic Response-Augmented Generation Framework for Personalized Conversational Systems

arXiv:2606. 21097v2 Announce Type: replace-cross Abstract: Deploying highly capable personalized conversational agents in resource-constrained or privacy-sensitive environments remains a significant challenge.

By Junfeng Liu, Christopher T. Symons, Ranga Raju Vatsavai

llms agents fine-tuning benchmarks safety

arXiv Machine Learning

Jun 26

Escaping Iterative Parameter-Space Noise: Differentially Private Learning with a Hypernetwork

arXiv:2606. 26772v1 Announce Type: new Abstract: Differentially private (DP) training of neural networks is often hindered by the large amount of noise required by gradient-based methods such as DP-SGD, which repeatedly inject high-dimensional noise in parameter space throughout training.

By Naoki Nishikawa, Shokichi Takakura, Satoshi Hasegawa

rag diffusion fine-tuning safety

arXiv AI

Jun 26

Metaphors are a Source of Cross-Domain Misalignment of Large Reasoning Models

arXiv:2601. 03388v3 Announce Type: replace-cross Abstract: Earlier research has shown that metaphors influence human decision-making, raising the question of whether metaphors also influence large language models (LLMs)' reasoning pathways, given that their training data contain a large number of metaphors.

By Zhibo Hu, Chen Wang, Yanfeng Shu, Hye-young Paik, Liming Zhu

llms fine-tuning safety

arXiv AI

Jun 26

Localizing RL-Induced Tool Use to a Single Crosscoder Feature

arXiv:2606. 26474v1 Announce Type: cross Abstract: Fine-tuning through RL reshapes the internal representations of language models to enable agentic behaviors such as tool use, yet the mechanistic basis of these changes remains poorly understood.

By Andrii Shportko, Shubham Bhokare, Ahmed Zeyad A Alzahrani, Bowen Cheng, Gustavo Mercier, Jessica Hullman

llms agents reinforcement-learning fine-tuning

arXiv AI

Jun 26

Assert, don't describe: Linguistic features that shift LLM reasoning about animal welfare

arXiv:2606. 26104v1 Announce Type: cross Abstract: Animal-welfare advocates produce a lot of writing, and increasingly that writing trains the language models that millions of people then ask about animal welfare.

By Jasmine Brazilek, Harper Dunn

llms fine-tuning benchmarks

arXiv AI

Jun 26

Helpfulness Hurts: Domain-Dependent Degradation of Mid-Trained Compassion Values Under Post-Training

arXiv:2606. 26102v1 Announce Type: cross Abstract: Standard post-training pipelines apply supervised fine-tuning (SFT) and reinforcement learning (RL) to make language models helpful, but these processes may inadvertently degrade values instilled during pre-training.

By Jasmine Brazilek, Juliana Seawell

llms reinforcement-learning fine-tuning benchmarks

arXiv Machine Learning

Jun 26

RSPC: A Benchmark for Modeling Stress and Psychiatric Conditions in Digitally Mediated Relationships using Psychiatrist Annotations

arXiv:2606. 27247v1 Announce Type: new Abstract: In NLP, mental health conditions are often modeled as isolated phenomena, without interpersonal context.

By Parmitha Vangapandu, Sai Ganesh Mokkapati, Sathwik Narkedimilli, MSVPJ Sathvik, Timothy Liu, Simon See, Johannes C. Eichstaedt

llms nlp fine-tuning benchmarks

arXiv AI

Jun 26

Inherited Circuits, Learned Semantics: How Fine-Tuning Creates Evasion Vulnerabilities Invisible to Standard Evaluation

arXiv:2606. 27091v1 Announce Type: cross Abstract: LLMs fine-tuned for security classification are usually evaluated on held-out examples from the same distribution as their training data.

By Ryan Fetterman

llms fine-tuning benchmarks

arXiv Machine Learning

Jun 26

A Multi-Fidelity Convolutional Autoencoder-Transfer Learning Framework for Guided-Wave-Based Damage Diagnosis Using Large Simulated and Limited Experimental Datasets

arXiv:2606. 27304v1 Announce Type: new Abstract: Guided wave-based structural health monitoring (GWSHM) with onboard transducers offers significant potential for the early diagnosis of damage in engineering structures.

By Santosh Kapuria, Abhishek

fine-tuning

arXiv AI

Jun 26

From Lexicon to AI: A Structured-Data Pipeline for Specialized Conversational Systems in Low-Resource Languages

arXiv:2606. 26112v1 Announce Type: cross Abstract: Low-resource languages face a critical challenge in AI development: creating specialized conversational systems without access to massive training corpora.

By Siddhant Hitesh Mantri, Dhara Gorasiya, Malhar Kulkarni, Pushpak Bhattacharya

llms fine-tuning efficiency data-engineering

arXiv AI

Jun 26

Limited Reference, Reliable Generation: A Two-Component Framework for Tabular Data Generation in Low-Data Regimes

arXiv:2509. 09960v2 Announce Type: replace-cross Abstract: Synthetic tabular data generation is increasingly essential in machine learning, supporting downstream applications when real-world, high-quality tabular data is insufficient.

By Mingxuan Jiang, Keyang Chen, Yongxin Wang, Yongsheng Zhao, Ziyue Dai, Yicun Liu, Zeping Li, Qiuyang Zhang, Hongyi Nie, Hongbin Zhu, Sen Liu, Guangnan Ye, Hongfeng Chai

llms diffusion fine-tuning benchmarks safety

arXiv AI

Jun 26

AIGP: An LLM-Based Framework for Long-Term Value Alignment in E-Commerce Pricing

arXiv:2606. 26787v1 Announce Type: cross Abstract: Traditional dynamic pricing models in large-scale e-commerce suffer from limited interpretability, poor utilization of unstructured information, and misalignment with long-term business objectives such as cumulative Gross Merchandise Value (GMV), Return on Investment (ROI) and milestone achievement.

By Chennan Ma, Yanning Zhang, Siqi Hong, Xiuchong Wang, Fei Xiao, Keping Yang

llms reinforcement-learning fine-tuning efficiency safety

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