Fine-tuning and adaptation

LoRA, PEFT, instruction tuning and domain adaptation — adapting a pretrained model without paying to train one.

3,507 stories · RSS feed

arXiv AI

Jul 28

Understanding Human-like Solutions in Combinatorial Optimization via Learning and Search

arXiv:2607. 23854v1 Announce Type: new Abstract: Humans often find good solutions to combinatorial optimization problems that are computationally hard even for advanced computer algorithms.

By Haijiang Yan, Jian-Qiao Zhu, Liqiang Huang, Ming Meng

reinforcement-learning fine-tuning

arXiv AI

Jul 28

SafeCRS: Personalized Safety Alignment for LLM-Based Conversational Recommender Systems

arXiv:2603. 03536v2 Announce Type: replace-cross Abstract: Current LLM-based conversational recommender systems (CRS) primarily optimize recommendation accuracy and user satisfaction.

By Haochang Hao, Yifan Xu, Xinzhuo Li, Yingqiang Ge, Lu Cheng

llms fine-tuning benchmarks safety

arXiv AI

Jul 28

Inference-Time Consensus for Mitigating Hidden Behaviors from LLM Fine-Tuning

arXiv:2607. 23394v1 Announce Type: new Abstract: Recent work shows that fine-tuning language models on even a small amount of poisoned data can install targeted misbehavior, and ostensibly benign data can transmit hidden preferences that generalize broadly.

By Adhyyan Narang, Artin Tajdini, Claire Zhang, Jamie Morgenstern

llms fine-tuning

arXiv AI

Jul 28

Flick: Few Labels Text Classification using K-Aware Intermediate Learning in Multi-Task Low-Resource Languages

arXiv:2506. 10292v2 Announce Type: replace-cross Abstract: Training deep learning networks with minimal supervision has gained significant research attention due to its potential to reduce reliance on extensive labelled data.

By Ali Almutairi, Abdullah Alsuhaibani, Shoaib Jameel, Aditya Joshi, Gelareh Mohammadi, Imran Razzak

llms nlp fine-tuning

arXiv AI

Jul 28

AIFL: A Global Daily Streamflow Forecasting Model Using a Deterministic LSTM Pre-trained on ERA5-Land and Fine-tuned on IFS

arXiv:2602. 16579v2 Announce Type: replace-cross Abstract: Reliable global streamflow forecasting is essential for flood preparedness and water resource management, yet data-driven models often suffer from a performance gap when transitioning from historical reanalysis to operational forecast products.

By Maria Luisa Taccari, Kenza Tazi, Ois\'in M. Morrison, Andreas Grafberger, Juan Colonese, Corentin Carton de Wiart, Christel Prudhomme, Cinzia Mazzetti, Matthew Chantry, Florian Pappenberger

fine-tuning benchmarks

arXiv AI

Jul 28

Continual Knowledge Consolidation LORA for Domain Incremental Learning

arXiv:2510. 16077v2 Announce Type: replace-cross Abstract: Domain Incremental Learning (DIL) is a sub-branch of continual learning that aims to address the never-ending arrival of new domains without catastrophic forgetting.

By Naeem Paeedeh, Mahardhika Pratama, Weiping Ding, Jimmy Cao, Wolfgang Mayer, Ryszard Kowalczyk, Ary Shiddiqi

fine-tuning benchmarks safety

arXiv AI

Jul 28

Nanbeige4.2-3B: Unlocking Agentic Capabilities in a Compact Model

arXiv:2607. 22083v2 Announce Type: replace Abstract: We present Nanbeige4.

By Nanbeige Lab, :, Chen Yang, Chengrui Huang, Fufeng Lan, Hanhui Chen, Hao Zhou, Huatong Song, Jiaqi Cao, Jiaying Zhu, Jinlin Niu, Kai Wang, Lisheng Huang, Qiliang Liang, Ran Le, Ruixiang Feng, Shuang Sun, Tao Gu, Tao Zhang, Tianyu Luo, Yang Song, Yun Xing, Yuntao Wen, Ziyao Xu, Zongchao Chen, Zongqiang Li

llms rag agents reinforcement-learning fine-tuning benchmarks safety

arXiv AI

Jul 28

Co-Harness: Co-Evolving Harnesses and Model Weights for LLM Agents

arXiv:2607. 22688v1 Announce Type: new Abstract: Post-training agents for automated AI research requires optimizing not only model parameters, but also the runtime harness that shapes how research trajectories are generated, evaluated, and learned from.

By Zhengyu Chen, Teng Xiao, Huaisheng Zhu, Yige Yuan, Luan Zhang, Jingang Wang

llms agents fine-tuning

arXiv AI

Jul 28

How LLM Task-Adaptation Reshapes Alignment: A Multi-dimensional Study of Behavioral and Representational Drift

arXiv:2607. 22676v1 Announce Type: new Abstract: Post-training is a key mechanism for adapting large language models to downstream tasks.

By James Elcock, William F. Shen, Xinchi Qiu, Nicholas D. Lane

llms reinforcement-learning fine-tuning safety

arXiv AI

Jul 28

Stress-Testing EEG Foundation Models for Clinical Decoding: Dataset Identity and Targeted Negative Controls

arXiv:2607. 24519v1 Announce Type: cross Abstract: Pretrained EEG foundation models are increasingly proposed for clinical decoding, but their transfer across populations and robustness to negative controls remain unclear.

By Marzieh Zare

rag fine-tuning benchmarks

arXiv AI

Jul 28

Realizing Scaling Laws in Recommender Systems: A Foundation-Expert Paradigm for Hyperscale Model Deployment

arXiv:2508. 02929v3 Announce Type: replace-cross Abstract: Scaling laws have been established for recommender systems, yet efficiently deploying foundation model (FM) across multiple recommendation surfaces remains a major unsolved challenge.

By Dai Li, Kevin Course, Wei Li, Hongwei Li, Jie Hua, Yiqi Chen, Zhao Zhu, Rui Jian, Xuan Cao, Bi Xue, Yu Shi, Jing Qian, Kai Ren, Matt Ma, Qunshu Zhang, Rui Li

rag fine-tuning efficiency

arXiv AI

Jul 28

Bayesian-LoRA: Probabilistic Low-Rank Adaptation of Large Language Models

arXiv:2601. 21003v3 Announce Type: replace Abstract: Large Language Models usually put more emphasis on accuracy and therefore, will guess even when not certain about the prediction, which is especially severe when fine-tuned on small datasets due to the inherent tendency toward miscalibration.

By Moule Lin, Shuhao Guan, Andrea Patane, David Gregg, Goetz Botterweck

llms fine-tuning benchmarks

arXiv AI

Jul 28

CHS-SQL: A Text-to-SQL approach based on Confidence-Guided Heuristic Search Schema Linking process

arXiv:2607. 22624v1 Announce Type: new Abstract: Recently, there have been several works in the Text-to-SQL domain that utilize Small Language Models (SLMs) for training.

By Minghao Yang, Yanjun Xu

llms fine-tuning benchmarks data-engineering

arXiv AI

Jul 28

The Visual Bottleneck: Sparse-Frame Adaptation of MLLMs for Joint Spatial-Temporal Video Grounding

arXiv:2607. 24570v1 Announce Type: cross Abstract: Large-scale video platforms process millions of uploads hourly, requiring moderation systems that can localize when and where policy violations occur within each video.

By Jiameng Zhang, Srikanth Madikeri

llms fine-tuning multimodal benchmarks

arXiv Machine Learning

Jul 28

Transfer learning for conflict and duplicate detection in software requirement pairs

arXiv:2301. 03709v3 Announce Type: replace-cross Abstract: Consistent and holistic expression of software requirements is important for the success of software projects.

By Garima Malik, Savas Yildirim, Mucahit Cevik

llms nlp fine-tuning

arXiv AI

Jul 28

PathScale-R1: Cross-scale Reasoning for Pathological Image Analysis

arXiv:2607. 23794v1 Announce Type: cross Abstract: Pathological diagnosis is inherently multi-scale, requiring the integration of global tissue architecture at low magnification with cellular morphology at higher magnification.

By Chi Phan, Tianyi Zhang, Yufeng Wu, Qiaochu Xue, Jiajie Zhang, Linghan Cai, Zeyu Liu, Sudong Wang, Yueming Jin, Dan Hu

llms nlp reinforcement-learning fine-tuning efficiency multimodal benchmarks safety

arXiv AI

Jul 28

EgoPlay: Event-Triggered Video Editing for Egocentric Streams

arXiv:2607. 24560v1 Announce Type: cross Abstract: We introduce EgoPlay, an event-triggered video-to-video editor for egocentric streams, obtained by fine-tuning a pretrained V2V diffusion transformer on event-conditioned data built primarily from Ego4D.

By Jinjie Mai, Gordon Guocheng Qian, Willi Menapace, Arpit Sahni, Chaoyang Wang, Ashkan Mirzaei, Runjia Li, Sergey Tulyakov, Bernard Ghanem, Peter Wonka, Rameen Abdal

llms diffusion fine-tuning multimodal benchmarks

arXiv AI

Jul 28

LC-SEPLM: long-range contact-supervised adaptation for sequence-only protein representation learning

arXiv:2607. 22777v1 Announce Type: cross Abstract: Protein language models learn transferable sequence representations.

By Chen Wang, Boming Kang, Qinghua Cui

llms fine-tuning benchmarks

arXiv AI

Jul 28

LuxInstruct: A Cross-Lingual Instruction Tuning Dataset For Luxembourgish

arXiv:2510. 07074v2 Announce Type: replace-cross Abstract: Instruction tuning has become a key technique for enhancing the performance of large language models, enabling them to better follow human prompts.

By Fred Philippy, Laura Bernardy, Siwen Guo, Jacques Klein, Tegawend\'e F. Bissyand\'e

llms nlp fine-tuning safety

arXiv AI

Jul 28

Through the Bottleneck: How Multi-head Latent Attention Separates Content from Position in Language Models

arXiv:2607. 23054v1 Announce Type: cross Abstract: Multi-head Latent Attention (MLA), introduced in DeepSeek-V2, compresses key-value pairs through a shared low-rank bottleneck (cKV), achieving 81% KV-cache reduction during inference.

By Dhruvil S, Fenil Sojitra, Ravirajsinh Chauhan

llms fine-tuning safety

Get the daily briefing