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 Machine Learning

Jun 2

Towards Sparse Video Understanding and Reasoning

arXiv:2602. 13602v2 Announce Type: replace-cross Abstract: We present \revise (\underline{Re}asoning with \underline{Vi}deo \underline{S}parsity), a multi-round agent for video question answering (VQA).

By Chenwei Xu, Zhen Ye, Shang Wu, Weijian Li, Zihan Wang, Zhuofan Xia, Lie Lu, Pranav Maneriker, Fan Du, Manling Li, Han Liu

llms agents nlp fine-tuning multimodal benchmarks

arXiv AI

Jun 2

GiPL: Generative augmented iterative Pseudo-Labeling for Cross-Domain Few-Shot Object Detection

arXiv:2605. 29539v2 Announce Type: replace-cross Abstract: Vision-language foundation models have shown promising zero-shot generalization for Cross-Domain Few-Shot Object Detection (CD-FSOD).

By Jiacong Liu, Shu Luo, Yikai Qin, Yaze Zhao, Yongwei Jiang, Yixiong Zou

llms computer-vision fine-tuning multimodal benchmarks

arXiv AI

Jun 2

LinguIUTics at PsyDefDetect: Iterative Imbalance-Aware Fine-tuning of Qwen3-8B for Psychological Defense Mechanism Classification

arXiv:2606. 00647v1 Announce Type: cross Abstract: Detecting psychological defense mechanisms in conversational text remains a challenging clinical NLP problem.

By Shefayat E Shams Adib, Ahmed Alfey Sani, Md Hasibur Rahman Alif, Ajwad Abrar

llms nlp fine-tuning benchmarks safety

arXiv Machine Learning

Jun 2

Drifting Preference Optimization for One-Step Generative Models

arXiv:2606. 02521v1 Announce Type: new Abstract: One-step text-to-image generators are attractive for deployment because they generate an image with a single forward pass, but preference finetuning them remains difficult: standard alignment methods often rely on policy likelihoods, denoising trajectories, differentiable reward gradients, or test-time optimization.

By Zhou Jiang, Yandong Wen, Zhen Liu

diffusion fine-tuning benchmarks safety

arXiv AI

Jun 2

An Open-Source Benchmark and Baseline for Multi-temporal Referring Segmentation

arXiv:2606. 00987v1 Announce Type: cross Abstract: Large Vision-Language Models (LVLMs) have shown strong visual understanding and language-guided grounding abilities, yet their capacity for multi-temporal visual reasoning remains underexplored.

By Bingyu Li, Da Zhang, Tao Huo, Zhiyuan Zhao, Junyu Gao, Xuelong Li

llms agents computer-vision fine-tuning multimodal benchmarks

arXiv AI

Jun 2

Value-Free Policy Optimization via Reward Partitioning

arXiv:2506. 13702v4 Announce Type: replace-cross Abstract: Single-trajectory preference optimization methods learn from datasets of ((prompt, response, reward)) tuples, offering a practical alternative to pairwise preference learning by directly leveraging scalar feedback.

By Bilal Faye, Hanane Azzag, Mustapha Lebbah

llms reinforcement-learning fine-tuning

arXiv AI

Jun 2

Erased but Not Forgotten: How Backdoors Compromise Concept Erasure

arXiv:2504. 21072v2 Announce Type: replace-cross Abstract: The expansion of text-to-image diffusion models has raised concerns about harmful outputs, from fabricated depictions of public figures to sexually explicit imagery.

By Tobias Braun, Jonas Henry Grebe, Marcus Rohrbach, Anna Rohrbach

diffusion fine-tuning benchmarks

arXiv AI

Jun 2

TInR: Exploring Tool-Internalized Reasoning in Large Language Models

arXiv:2604. 10788v2 Announce Type: replace-cross Abstract: Tool-Integrated Reasoning (TIR) has emerged as a promising direction by extending Large Language Models' (LLMs) capabilities with external tools during reasoning.

By Qiancheng Xu, Yongqi Li, Fan Liu, Hongru Wang, Min Yang, Wenjie Li

llms reinforcement-learning fine-tuning safety

arXiv Machine Learning

Jun 2

Learning To Sample From Diffusion Models Via Inverse Reinforcement Learning

arXiv:2602. 08689v2 Announce Type: replace Abstract: Diffusion models generate samples through an iterative denoising process guided by a pretrained neural network.

By Constant Bourdrez, Alexandre V\'erine, Olivier Capp\'e

diffusion reinforcement-learning fine-tuning

arXiv AI

Jun 2

Reward Score Matching: Unifying Reward-based Fine-tuning for Flow and Diffusion Models

arXiv:2604. 17415v3 Announce Type: replace-cross Abstract: Reward-based fine-tuning steers a pretrained diffusion or flow-based generative model toward higher-reward samples while remaining close to the pretrained model.

By Jeongjae Lee, Jinho Chang, Jeongsol Kim, Jong Chul Ye

diffusion fine-tuning safety

arXiv Machine Learning

Jun 2

Task-Induced Representational Invariances Depend on Learning Objective in Deep RL

arXiv:2606. 01868v1 Announce Type: new Abstract: Reinforcement Learning (RL) has long served as a model for goal-directed animal behavior in neuroscience.

By Manu Srinath Halvagal, Sebastian Lee, SueYeon Chung

llms reinforcement-learning fine-tuning

arXiv AI

Jun 2

Subliminal Learning Is Steering Vector Distillation

arXiv:2606. 00995v1 Announce Type: new Abstract: Subliminal learning refers to a student language model acquiring a teacher's traits (e.

By Camila Blank, Agam Bhatia, Senthooran Rajamanoharan, Arthur Conmy, Neel Nanda

llms fine-tuning efficiency

arXiv Machine Learning

Jun 2

BERT4beam: Large AI Model Enabled Generalized Beamforming Optimization

arXiv:2509. 11056v2 Announce Type: replace-cross Abstract: Artificial intelligence (AI) is anticipated to emerge as a pivotal enabler for the forthcoming sixth-generation (6G) wireless communication systems.

By Yuhang Li, Yang Lu, Wei Chen, Bo Ai, Zhiguo Ding

llms nlp fine-tuning

arXiv Machine Learning

Jun 2

OncoReason: Structuring Clinical Reasoning in LLMs for Robust and Interpretable Survival Prediction

arXiv:2510. 17532v2 Announce Type: replace-cross Abstract: Predicting cancer treatment outcomes requires models that are both accurate and interpretable, particularly in the presence of heterogeneous clinical data.

By Raghu Vamshi Hemadri, Geetha Krishna Guruju, Kristi Topollai, Anna Ewa Choromanska

llms nlp reinforcement-learning fine-tuning benchmarks safety

arXiv AI

Jun 2

U-Cast: A Surprisingly Simple and Efficient Frontier Probabilistic AI Weather Forecaster

arXiv:2604. 09041v2 Announce Type: replace-cross Abstract: AI-based weather forecasting now rivals traditional physics-based ensembles, but state-of-the-art (SOTA) models rely on specialized architectures and massive computational budgets, creating a high barrier to entry.

By Salva R\"uhling Cachay, Duncan Watson-Parris, Rose Yu

diffusion fine-tuning efficiency benchmarks

arXiv AI

Jun 2

TRON: Targeted Rule-Verifiable Online Environments for Visual Reasoning RL

arXiv:2606. 01599v1 Announce Type: new Abstract: Reinforcement learning (RL) for visual reasoning needs scalable, verifiable, and controllable training signals.

By Tianze Yang, Yucheng Shi, Ruitong Sun, Jingyuan Huang, Ninghao Liu, Jin Sun

reinforcement-learning fine-tuning multimodal benchmarks

arXiv AI

Jun 2

Hallucination Detection-Guided Preference Optimization for Clinical Summarization

arXiv:2605. 28910v2 Announce Type: replace-cross Abstract: Large language models (LLMs) have shown promise on summarization tasks, but they often produce hallucinations, which are unsupported or incorrect statements that limit their reliability in specialized healthcare applications.

By Shamanth Kuthpadi Seethakantha, Dung Ngoc Thai, Vara Prasad Gudi, Simran Tiwari, Rami Matar, Avijit Mitra, Wenlong Zhao, Andrew McCallum, Wael Salloum

llms nlp fine-tuning safety

arXiv AI

Jun 2

ToolSelf: Unifying Task Execution and Self-Reconfiguration via Tool-Driven Emergent Adaptation

arXiv:2602. 07883v3 Announce Type: replace Abstract: LLM-powered agentic systems excel at complex long-horizon tasks, but remain constrained by static configurations fixed before execution.

By Jingqi Zhou, Sheng Wang, Dezhao Deng, Junwen Lu, Junwei Su, Qintong Li, Jiahui Gao, Hao Wu, Jiyue Jiang, Lingpeng Kong, Dunhong Jin, Chuan Wu

llms agents reinforcement-learning fine-tuning benchmarks

arXiv AI

Jun 2

Pause and Think: A Dataset and Benchmark for Video-Grounded Assistive Action Suggestion

arXiv:2606. 00616v1 Announce Type: cross Abstract: Recent Vision-Language Models (VLMs) struggle with grounded reasoning, temporal consistency, and context aware planning in videos.

By Shivam Singh, Saptarshi Majumdar, Pratik Prabhanjan, Zicheng Liu, Emad Barsoum

llms fine-tuning multimodal benchmarks

arXiv AI

Jun 2

Cast a Wider Net: Coordinated Pass@K Policy Optimization for Code Reasoning

arXiv:2605. 27000v2 Announce Type: replace-cross Abstract: Repeated sampling with a verifier is the standard way to allocate test-time compute for code generation, with pass@$K$ as the canonical metric.

By Yilong Li, Suman Banerjee, Tong Che

reinforcement-learning fine-tuning benchmarks

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