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 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 AI

Jun 2

Closed-Loop Neural Activation Control in Vision-Language-Action Models

arXiv:2606. 00269v1 Announce Type: new Abstract: Vision-Language-Action (VLA) models can be steered at test time by intervening on semantically meaningful internal directions, but existing methods use a fixed steering coefficient, effectively operating in open loop.

By Abhijith Babu, Ramneet Kaur, Nathaniel D. Bastian, Olivera Kotevska, Susmit Jha, Yanzhao Wu, Sumit Kumar Jha, Anirban Roy

reinforcement-learning robotics fine-tuning multimodal

arXiv Machine Learning

Jun 2

Safety Game: Inference-Time Alignment of Black-Box LLMs via Constrained Optimization

arXiv:2510. 09330v3 Announce Type: replace Abstract: Ensuring that large language models (LLMs) comply with safety requirements is a central challenge in AI deployment.

By Tuan Nguyen, Long Tran-Thanh

llms agents reinforcement-learning fine-tuning safety

arXiv Machine Learning

Jun 2

Semi-Supervised Noise Adaptation: Transferring Knowledge from Noise Domain

arXiv:2606. 00558v1 Announce Type: new Abstract: Transfer learning aims to facilitate the learning of a target domain by transferring knowledge from a source domain.

By Yuan Yao, Jin Song, Huixia Li, Tongtong Yuan, Jiaqi Wu, Yu Zhang

fine-tuning

arXiv Machine Learning

Jun 2

Self-Improving Small Object Grounding in LVLMs

arXiv:2606. 01612v1 Announce Type: cross Abstract: Can internal attention patterns in Large Vision Language Models (LVLMs) identify reliable small-object boxes without fine-tuning?

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

llms fine-tuning safety

arXiv AI

Jun 2

MASER: Modality-Adaptive Specialist Routing for Embodied 3D Spatial Intelligence

arXiv:2606. 02463v1 Announce Type: cross Abstract: In 3D environments, Embodied Agents answer spatially relevant questions through reasoning from a mixture of modalities including natural language, RGB images, point clouds, depth maps and camera poses.

By Hilton Raj, Vishnuram AV

llms rag agents robotics fine-tuning multimodal benchmarks

arXiv Machine Learning

Jun 2

LRAgent: Efficient KV Cache Sharing for Multi-LoRA LLM Agents

arXiv:2602. 01053v2 Announce Type: replace Abstract: Role specialization in multi-LLM agent systems is often realized via multi-LoRA, where agents share a pretrained backbone and differ only by lightweight adapters.

By Hyesung Jeon, Hyeongju Ha, Jae-Joon Kim

llms agents fine-tuning efficiency benchmarks

arXiv AI

Jun 2

ProductWebGen: Benchmarking Multimodal Product Webpage Generation

arXiv:2606. 01022v1 Announce Type: cross Abstract: Crafting a product display webpage from a source product image, along with layout and visual content instructions, holds significant practical value for domains such as marketing, advertising, and E-commerce.

By Zhihong Liu, Siqi Kou, Zheng Li, Ye Ma, Quan Chen, Peng Jiang, Kai Yu, Zhijie Deng

llms diffusion fine-tuning multimodal benchmarks

arXiv Machine Learning

Jun 2

Context-aware child-directed speech detection from long-form recordings

arXiv:2606. 01134v1 Announce Type: cross Abstract: Automatically distinguishing child-directed speech from adult-directed speech in long-form recordings is key to scalable analyses of children's language environments.

By Th\'eo Charlot, Tarek Kunze, Kaveri K. Sheth, Alejandrina Cristia, Marvin Lavechin

computer-vision fine-tuning

arXiv Machine Learning

Jun 2

Latent Diffusion Pretraining for Crystal Property Prediction

arXiv:2606. 00776v1 Announce Type: new Abstract: Fast and accurate prediction of crystal properties is a central challenge in new materials design.

By Shrimon Mukherjee, Kishalay Das, Partha Basuchowdhuri, Pawan Goyal, Niloy Ganguly

llms diffusion fine-tuning

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

GRASP: Plan-Guided Graph Retrieval with Adaptive Fusion and Reranking on Semi-Structured Knowledge Bases

arXiv:2605. 30237v2 Announce Type: replace-cross Abstract: Semi-structured knowledge bases (SKBs) embed textual documents in a typed graph of entities and relations, and underpin applications such as product search, academic paper search, and precision-medicine inquiries.

By Yicheng Tao, Yiqun Wang, Xiangchen Song, Xin Luo, Kai Liu, Jie Liu

rag fine-tuning benchmarks

arXiv AI

Jun 2

Calibrating Uncertainty for Zero-Shot Adversarial CLIP

arXiv:2512. 12997v2 Announce Type: replace-cross Abstract: CLIP delivers strong zero-shot classification but remains highly vulnerable to adversarial attacks.

By Wenjing Lu, Zerui Tao, Yuning Qiu, Dongping Zhang, Yang Yang, Qibin Zhao

fine-tuning benchmarks safety

arXiv AI

Jun 2

Ryze: Evidence-Enriched Data Synthesis from Biomedical Papers

arXiv:2606. 00902v1 Announce Type: new Abstract: General-purpose VLMs remain unreliable for biomedical research because valid answers in scientific papers depend on evidence split across figures, tables, charts, captions, and referring text.

By Yeqi Huang, Yue Chen, Yanwei Ye, Guanhao Su, Luo Mai

llms computer-vision reinforcement-learning fine-tuning multimodal

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

Hugging Face Trending Papers

Jun 1

Drifting Preference Optimization for One-Step Generative Models

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. We propose Drifting Preference Optimization (DrPO), an online preference-finetuning method for deterministic one-step generators.

diffusion fine-tuning benchmarks safety

Hugging Face Trending Papers

Jun 1

Active Exploring like a Pigeon: Reinforcing Spatial Reasoning via Agentic Vision-Language Models

Enabling Vision-Language Models (VLMs) to perform spatial reasoning remains challenging. Existing approaches treat VLMs as passive observers, which is difficult for real-world applications.

llms agents reinforcement-learning fine-tuning multimodal benchmarks

Hugging Face Trending Papers

Jun 1

Honey, I Shrunk the Arc de Triomphe!

Metric scale monocular geometry estimation has seen significant progress through large-scale data aggregation, yet current foundation models suffer from a persistent ''scale-collapse'' phenomenon: distant landmarks and vast landscapes are metrically underestimated. We hypothesize that this performance gap stems from a training data bottleneck, where existing metric-scale datasets are hardware-constrained to homogenous vehicle-captured LiDAR or short-range indoor scans, or consist of synthetic data that lacks the semantic complexity of the physical world.

fine-tuning benchmarks

Hugging Face Trending Papers

Jun 1

TalkTag: Fine-Grained Morphosyntactic Error Annotation for Transcribed Speech

Fine-grained morphosyntactic error annotation is important in clinical and developmental language research, yet it is labour-intensive, expert-dependent, and difficult to scale. We present TalkTag, an LLM-based lightweight tool fine-tuned to automate CHAT-style error annotation in spoken-language transcripts.

llms fine-tuning

Berkeley AI Research

May 8

Adaptive Parallel Reasoning: The Next Paradigm in Efficient Inference Scaling

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llms rag agents nlp reinforcement-learning fine-tuning efficiency benchmarks

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