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 6

TAM: Torque Adaptation Module for Robust Motion Transfer in Manipulation

arXiv:2606. 06218v1 Announce Type: cross Abstract: A policy tuned for one robot often behaves differently on another, whether due to the sim-to-real gap, unknown payloads, or the differing dynamics of two instances of the same robot.

By Dongwon Son, Florian Shkurti, Jason Lee, Naman Shah, Beomjoon Kim, Dieter Fox

reinforcement-learning robotics fine-tuning

arXiv AI

Jun 6

Towards Generalization of Block Attention via Automatic Segmentation and Block Distillation

arXiv:2605. 15913v4 Announce Type: replace-cross Abstract: Block attention, which processes the input as separate blocks that cannot attend to one another, offers significant potential to improve KV cache reuse in long-context scenarios such as Retrieval-Augmented Generation (RAG).

By Shuaiyi Li, Zhisong Zhang, Yan Wang, Lei Zhu, Dongyang Ma, Chenlong Deng, Yang Deng, Wai Lam

rag computer-vision fine-tuning efficiency benchmarks

arXiv AI

Jun 6

Emotion-Aware Image Generation from Korean Diary Text via LLM-based Prompt Translation and LoRA Fine-Tuning

arXiv:2606. 05816v1 Announce Type: cross Abstract: T2I models cannot effectively capture sentiment from various types of text, including diaries, as they primarily focus on visual object-related patterns rather than contextual emotional understanding.

By Jihun Cho, Soo-Yeon Jeong, Sun-Young Ihm

llms diffusion fine-tuning benchmarks

arXiv AI

Jun 6

A Study of LLMs' Preferences for Libraries and Programming Languages

arXiv:2503. 17181v4 Announce Type: replace-cross Abstract: Despite the rapid progress of large language models (LLMs) in code generation, existing evaluations focus on functional correctness or syntactic validity, overlooking how LLMs make critical design choices such as which library or programming language to use.

By Lukas Twist, Mark Harman, Don Syme, Joost Noppen, Helen Yannakoudakis, Detlef Nauck, Jie M. Zhang

llms fine-tuning benchmarks

arXiv AI

Jun 6

Where Should Knowledge Enter? A Layered Framework for Knowledge Infusion in Multimodal Iterative Generative Mo

arXiv:2606. 06356v1 Announce Type: new Abstract: Multimodal generative models produce fluent outputs but remain unreliable when generation must respect structured, domain-specific, or safety-critical knowledge.

By Renjith Prasad, Chathurangi Shyalika, Anushka Pawar, Amit Sheth

diffusion fine-tuning multimodal safety

arXiv AI

Jun 6

Code2LoRA: Hypernetwork-Generated Adapters for Code Language Models under Software Evolution

arXiv:2606. 06492v1 Announce Type: cross Abstract: Code language models need repository-level context to resolve imports, APIs, and project conventions.

By Liliana Hotsko, Yinxi Li, Yuntian Deng, Pengyu Nie

llms rag fine-tuning benchmarks

arXiv AI

Jun 6

FUSAR-GPT : A Spatiotemporal Feature-Embedded and Two-Stage Decoupled Visual Language Model for SAR Imagery

arXiv:2602. 19190v4 Announce Type: replace-cross Abstract: Research on the intelligent interpretation of all-weather, all-time Synthetic Aperture Radar (SAR) is crucial for advancing remote sensing applications.

By Xiaokun Zhang, Yi Yang, Ziqi Ye, Baiyun, Xiaorong Guo, Qingchen Fang, Ruyi Zhang, Xinpeng Zhou, Haipeng Wang

llms rag fine-tuning multimodal benchmarks

arXiv AI

Jun 6

Learning Adaptive Parallel Execution for Efficient Code Localization

arXiv:2601. 19568v2 Announce Type: replace Abstract: Code localization constitutes a key bottleneck in automated software development pipelines.

By Ke Xu, Siyang Xiao, Ming Liang, Yichen Yu, Zhixiang Wang, Jingxuan Xu, Dajun Chen, Wei Jiang, Yong Li

agents reinforcement-learning fine-tuning benchmarks

arXiv AI

Jun 6

LatentSkill: From In-Context Textual Skills to In-Weight Latent Skills for LLM Agents

arXiv:2606. 06087v1 Announce Type: cross Abstract: Agent systems increasingly use textual skills to encode reusable task procedures, but injecting these skills into the prompt at every step incurs substantial context overhead and exposes skill content as plaintext.

By Aofan Yu, Chenyu Zhou, Tianyi Xu, Zihan Guo, Rong Shan, Zhihui Fu, Jun Wang, Weiwen Liu, Yong Yu, Weinan Zhang, Jianghao Lin

llms agents fine-tuning

arXiv AI

Jun 6

ECI: Effective Contrastive Information to Evaluate Hard-Negatives

arXiv:2603. 20990v2 Announce Type: replace-cross Abstract: Hard-negative source selection for dense retrieval is usually decided only after fine-tuning and downstream evaluation.

By Aarush Sinha, Rahul Seetharaman, Aman Bansal

llms rag nlp fine-tuning safety

arXiv AI

Jun 6

Amortizing Federated Adaptation: Hypernetwork Driven LoRA for Personalized Foundation Models

arXiv:2606. 06154v1 Announce Type: new Abstract: Federated fine-tuning of foundation models using Low-Rank Adaptation (LoRA) offers a communication efficient solution for distributed learning.

By Sunny Gupta, Shambhavi Shanker, Amit Sethi

fine-tuning multimodal benchmarks safety

arXiv AI

Jun 6

Noise-Aware Visual Representation Learning for Medical Visual Question Answering

arXiv:2606. 05535v1 Announce Type: cross Abstract: Medical visual question answering (Med-VQA) has strong potential for clinical decision support by enabling AI models to interpret medical images and answer clinically relevant queries.

By I Putu Adi Pratama, Bahadorreza Ofoghi, Atul Sajjanhar, Shang Gao

llms rag nlp fine-tuning benchmarks

arXiv AI

Jun 6

Beyond Code Pairs: Dialogue-Based Data Generation for LLM Code Translation

arXiv:2512. 03086v2 Announce Type: replace-cross Abstract: Large language models (LLMs) have shown remarkable capabilities in code translation, yet their performance deteriorates in low-resource programming domains such as Fortran and emerging frameworks like CUDA, where high-quality parallel data are scarce.

By Le Chen, Nuo Xu, Winson Chen, Bin Lei, Pei-Hung Lin, Dunzhi Zhou, Rajeev Thakur, Caiwen Ding, Ali Jannesari, Chunhua Liao

llms fine-tuning

arXiv AI

Jun 6

From Reward-Hack Activations to Agentic Risk States: Context-Calibrated Mechanistic Monitoring in LLM Agents

arXiv:2606. 06223v1 Announce Type: new Abstract: Language-model agents act through repeated cycles of observation, reasoning, and action selection, making safety monitoring depend on both internal model state and environment context.

By Patrick Wilhelm, Odej Kao

llms agents fine-tuning safety

arXiv AI

Jun 6

Reward-Decomposed Reinforcement Learning for Immersive Video Role-Playing

arXiv:2605. 04733v2 Announce Type: replace Abstract: Text-based role-playing models can imitate character styles, but often fail to capture scene atmosphere and evolving tension, which are crucial for immersive applications such as VR games and interactive narratives.

By Miao Wang, Yuling Shi, Yijiang Li, Yeheng Chen, Xiaodong Gu, Bin Li, Bo Gao, Jun Wang, Zengxin Han, Jingtong Wu, Yaduan Ruan

llms reinforcement-learning fine-tuning multimodal benchmarks safety

arXiv AI

Jun 6

AdaMEM: Test-Time Adaptive Memory for Language Agents

arXiv:2606. 05684v1 Announce Type: new Abstract: A central challenge for language agents is utilizing past experience to adapt to dynamic test-time conditions.

By Yunxiang Zhang, Yiheng Li, Ali Payani, Lu Wang

agents fine-tuning

arXiv AI

Jun 6

DisasterBench: A Multimodal Benchmark for UAV-Based Disaster Response in Complex Environments

arXiv:2606. 06217v1 Announce Type: cross Abstract: When a disaster unfolds, responders must answer not only what is happening, but also why it is happening, what will happen next, and what to do now, often from noisy low-altitude UAV views and under tight on-site compute constraints.

By Tan Zhang, Quanyou Li, Lu Zhang, Jun Liu, Xiaofeng Zhu, Ping Hu

llms reinforcement-learning fine-tuning multimodal benchmarks safety

arXiv AI

Jun 6

Drive-KD: Multi-Teacher Distillation for VLMs in Autonomous Driving

arXiv:2601. 21288v2 Announce Type: replace Abstract: Autonomous driving is an important and safety-critical task, and recent advances in LLMs/VLMs have opened new possibilities for reasoning and planning in this domain.

By Weitong Lian, Zecong Tang, Haoran Li, Tianjian Gao, Yifei Wang, Zixu Wang, Lingyi Meng, Tengju Ru, Zhejun Cui, Yichen Zhu, Hangshuo Cao, Qi Kang, Tianxing Chen, Kaixuan Wang, Yu Zhang

llms agents robotics fine-tuning efficiency safety

arXiv AI

Jun 6

EGTR-Review: Efficient Evidence-Grounded Scientific Peer Review Generation via Multi-Agent Teacher Distillation

arXiv:2606. 06025v1 Announce Type: cross Abstract: Scientific peer review generation has attracted increasing attention for reducing reviewing burdens and providing timely feedback.

By Xinpeng Qiu, Wang Yihu, Zhifeng Liu, Xiaochen Wang, Jimin Wang

llms agents fine-tuning efficiency

arXiv AI

Jun 6

AgenticRL: Self-Refining Agentic Reinforcement Learning for Vision-Conditioned UAV Navigation

arXiv:2606. 03963v2 Announce Type: replace-cross Abstract: Deep reinforcement learning has shown strong potential for enabling autonomous robots to learn complex navigational tasks.

By Roohan Ahmed Khan, Yasheerah Yaqoot, Muhammad Ahsan Mustafa, Dzmitry Tsetserukou

llms agents reinforcement-learning robotics fine-tuning multimodal

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