Fine-tuning and adaptation

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

3,511 stories · RSS feed

arXiv AI

Jul 13

Point of Order: Action-Aware LLM Persona Modeling for Data-Grounded Civic Deliberation

arXiv:2511. 17813v3 Announce Type: replace-cross Abstract: LLM-based simulations can enable controlled studies of civic deliberation, but current systems lack speaker-attributed data and methods for evaluating long-form institutional behavior.

By Scott Merrill, Shashank Srivastava

llms fine-tuning multimodal

arXiv Machine Learning

Jul 13

Super-Tuning: From Activation-Aware Pruning to Sparse Fine-Tuning

arXiv:2607. 09287v1 Announce Type: new Abstract: Large language models (LLMs) remain expensive to fine-tune because full-parameter updates require substantial memory, compute, and per-task storage.

By Ivan Ilin, Philip Zmushko, Peter Richt\'arik

llms fine-tuning efficiency

arXiv Machine Learning

Jul 13

Pitfalls and Remedies for Multi-Task Bayesian Optimization

arXiv:2607. 09073v1 Announce Type: new Abstract: Bayesian optimization routinely warm-starts a target experiment with data from related source tasks, and the multi-task Gaussian process is the textbook surrogate for the job.

By Carl Hvarfner, Sam Daulton, Max Balandat, Eytan Bakshy

fine-tuning safety

arXiv AI

Jul 13

PAC-ACT: Post-training Actor-Critic for Action Chunking Transformers

arXiv:2607. 09590v1 Announce Type: cross Abstract: Precision industrial contact manipulation requires reliable robot policies under pose perturbations and contact-force constraints.

By Yujie Pang, Zudong Li

llms rag robotics fine-tuning efficiency multimodal benchmarks safety

arXiv AI

Jul 13

Deceptive Grounding: Entity Attribution Failure in Clinical Retrieval-Augmented Generation

arXiv:2607. 09349v1 Announce Type: cross Abstract: Retrieval-augmented generation evaluation checks whether model claims are factually grounded in retrieved documents.

By Cedric Caruzzo, Donggeun Yoo, Tae Soo Kim

rag fine-tuning benchmarks safety

arXiv AI

Jul 13

ReinforceGen: Hybrid Skill Policies with Automated Data Generation and Reinforcement Learning

arXiv:2512. 16861v2 Announce Type: replace-cross Abstract: Long-horizon manipulation has been a long-standing challenge in the robotics community.

By Zihan Zhou, Animesh Garg, Ajay Mandlekar, Caelan Garrett

reinforcement-learning robotics fine-tuning benchmarks

arXiv AI

Jul 13

Automatic Thematic Indexing of Large Literary Corpora: A Machine Learning Approach to Voltaire's Complete Works

arXiv:2607. 09316v1 Announce Type: cross Abstract: Thematic indexing -- the practice of assigning structured conceptual labels to sections of text -- is essential to scholarly access in large-scale literary and historical editions, yet it remains a largely manual, labour-intensive process.

By Miguel Arana-Catania, Gillian Pink, Glenn Roe

llms fine-tuning

arXiv AI

Jul 13

Multi-Conditioned Diffusion Synthesis of Sand Boils for Low-Resource Earthen-Levee Inspection

arXiv:2607. 08794v1 Announce Type: cross Abstract: Sand boils on earthen levees are safety-critical defects, but pixel-level detection is limited by scarce annotations.

By Padam Jung Thapa, Abdullah Bin Naeem, Ayon Dey, Anav Katwal, Md Tamjidul Hoque

diffusion computer-vision fine-tuning safety

arXiv AI

Jul 13

Multimodal Reward Hacking in Reinforcement Learning

arXiv:2607. 09492v1 Announce Type: new Abstract: Reinforcement learning (RL) is increasingly used to align multimodal large language models (MLLMs), but higher rewards do not always imply better task performance.

By Jiayu Yao, Yiwei Wang, Anmeng Zhang, Zhe Sun, Songsong Wang, Lingrui Mei, Yuyao Ge, Shenghua Liu

llms reinforcement-learning fine-tuning multimodal safety

arXiv Machine Learning

Jul 13

Hair-Trigger Alignment: Black-Box Evaluation Cannot Guarantee Post-Update Alignment

arXiv:2601. 22313v2 Announce Type: replace Abstract: Large Language Models (LLMs) are rarely static and are frequently updated in practice.

By Yavuz Bakman, Duygu Nur Yaldiz, Eleni Triantafillou, Peter Kairouz, Salman Avestimehr, Sai Praneeth Karimireddy

llms fine-tuning safety

arXiv AI

Jul 13

GrAInS: Gradient-based Attribution for Inference-Time Steering of LLMs and VLMs

arXiv:2507. 18043v2 Announce Type: replace-cross Abstract: Inference-time steering methods offer a lightweight alternative to fine-tuning large language models (LLMs) and vision-language models (VLMs) by modifying internal activations at test time without updating model weights.

By Duy Nguyen, Archiki Prasad, Elias Stengel-Eskin, Mohit Bansal

llms fine-tuning multimodal safety

arXiv AI

Jul 13

Towards Shutdownable Agents: Generalizing Stochastic Choice in RL Agents and LLMs

arXiv:2604. 17502v4 Announce Type: replace Abstract: Misaligned artificial agents might resist shutdown.

By Carissa Cullen, Harry Garland, Alexander Roman, Louis Thomson, Christos Ziakas, Elliott Thornley

llms agents reinforcement-learning fine-tuning

arXiv Machine Learning

Jul 13

RELISH: LLM REgression with a Latent Iterative State Head

arXiv:2604. 01206v2 Announce Type: replace-cross Abstract: We present RELISH (REgression with a Latent Iterative State Head), a novel, lightweight architecture designed for text regression with large language models.

By Yiheng Su, Matthew Lease

llms fine-tuning

arXiv Machine Learning

Jul 13

FlowDAgger: Human-in-the-Loop Adaptation of Generative Robot Policies in Latent Space

arXiv:2607. 08877v1 Announce Type: cross Abstract: Pretrained generative robot policies based on flow matching and diffusion have achieved impressive results across a wide range of manipulation tasks.

By Michael Murray, Daphne Chen, Simran Bagaria, Dean Fortier, Tess Hellebrekers, Galen Mullins, Harshavardhan Gajarla, Oier Mees, Maya Cakmak, Andrey Kolobov

diffusion reinforcement-learning robotics fine-tuning

arXiv AI

Jul 13

Leveraging Multi-Agent System (MAS) and Fine-Tuned Small Language Models (SLMs) for Automated Telecom Network Troubleshooting

arXiv:2511. 00651v2 Announce Type: replace Abstract: Telecom networks are rapidly growing in scale and complexity, making effective management, operation, and optimization increasingly challenging.

By Chenhua Shi, Bhavika Jalli, Gregor Macdonald, John Zou, Wanlu Lei, Mridul Jain, Joji Philip

llms agents fine-tuning

arXiv AI

Jul 13

Sticky Routing: Training MoE Models for Memory-Efficient Inference

arXiv:2607. 08780v1 Announce Type: cross Abstract: Mixture-of-Experts (MoE) models activate only a sparse subset of experts per token, yet consecutive tokens frequently activate different experts -- causing constant weight swapping between slow storage and fast memory on edge devices.

By Ali Kayyam

llms fine-tuning

arXiv AI

Jul 13

Eluna: An Agentic LLM System for Automating Warehouse Operations with Reasoning and Task Execution

arXiv:2607. 08960v1 Announce Type: cross Abstract: Warehouse operations are governed by Standard Operating Procedures (SOPs) that encode complex, multi-system decision logic, which must be executed reliably under strict time constraints, yet LLM agents lack mechanisms to enforce procedural compliance and degrade under the context overload full SOP specifications introduce.

By Ning Liu, Kalle Kujanp\"a\"a, Zhaoxuan Zhu, P Aditya Sreekar, Kaiwen Liu, Chuanneng Sun, Jorge Marchena Menendez, Matthew Bales, Tianyu Yang, Shahnawaz Alam, Rose Yu, Baoyuan Liu, Kristina Klinkner, Shervin Malmasi

llms agents fine-tuning efficiency benchmarks

arXiv AI

Jul 13

Blockchain-Linked Auditable Decision Management for Telecom/IoT Fraud-Control Requests

arXiv:2607. 09259v1 Announce Type: cross Abstract: Telecom fraud-control studies often stop at detector-level classification, but deployment use requires request-level policy resolution, lifecycle traceability, and auditability.

By Saviz Changizi, Nasibeh Mohammadzadeh, Mohammad Shojafar, Rahim Tafazolli

llms fine-tuning efficiency

arXiv AI

Jul 13

CLAP: Direct VLM-to-VLA Adaptation via Language-Action Grounding

arXiv:2607. 08974v1 Announce Type: cross Abstract: Vision-language-action models (VLAs) inherit semantic capabilities from pretrained VLMs, yet large-scale post-training on robot data and architectural modifications can reshape the backbone so extensively that it becomes difficult to isolate what the VLM contributes to control.

By Yuri Ishitoya, Jeremy Siburian, Masashi Hamaya, Kuniaki Saito, Cristian C. Beltran-Hernandez, Mai Nishimura

robotics fine-tuning multimodal

Hugging Face Trending Papers

Jul 13

MED-DSLC: Multi-Expert-Domain Classification via Domain Supervision and Logit Calibration

Vision-language models (VLMs) such as CLIP enable zero-shot classification by comparing image features with text prompts in a shared embedding space. A fundamental property underlying this capability is the global comparability of logits across arbitrary candidate classes.

llms rag fine-tuning multimodal benchmarks

Get the daily briefing