Fine-tuning and adaptation

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

8,208 stories · RSS feed

arXiv AI

Jul 22

Athena-Brain Technical Report: An Efficient Robot Brain for General Intelligence and Embodied Interactio

arXiv:2607. 18985v1 Announce Type: new Abstract: Large language models (LLMs) have demonstrated remarkable capabilities in language understanding, reasoning, and world knowledge.

By Jialian Li, Junhong Liu, Yuchen Cao, Weiran Guo, Jiaming Song, Xutao Wang, Yi Zhao, Jiangpin Liu, Jie Chen

llms agents reinforcement-learning robotics fine-tuning benchmarks

arXiv Machine Learning

Jul 22

One Student, Many Teachers: Multi-Task On-Policy Distillation via Soft-Prompt Privileged Context

arXiv:2607. 18293v1 Announce Type: new Abstract: On-policy self-distillation (OPSD) teaches large language models new skills through a teacher that shares the student's backbone and supervises its own rollouts.

By Yingzi Ma, Zichen Zhu, Ming Jiang, Chaowei Xiao

llms agents fine-tuning efficiency benchmarks

arXiv AI

Jul 22

Using LLMs for Explainable, Data-Driven Insight Generation from Time Series

arXiv:2607. 18271v1 Announce Type: new Abstract: Time series forecasts are widely used in decision-critical domains, where they are rarely consumed without accompanying explanations.

By Ria Mundhra, Gustavo Sato dos Santos, Michael Benedikt

llms fine-tuning safety

arXiv AI

Jul 22

Decoupled Pipeline with Proposal Reranking and Score Fusion for Positive-Unlabeled Marine Species Detection

arXiv:2607. 18700v1 Announce Type: cross Abstract: The FathomNetCLEF 2026 competition combines underwater object detection and fine-grained marine species classification under a positive-unlabeled evaluation setting.

By Robert James Brock, Sebastian Maximilian Krupa, Jason Kahei Tam

rag computer-vision fine-tuning benchmarks

arXiv AI

Jul 22

PRISP: Privacy-Safe Few-Shot Personalization via Lightweight Adaptation

arXiv:2601. 06471v2 Announce Type: replace-cross Abstract: Large language model (LLM) personalization aims to adapt general-purpose models to individual users.

By Junho Park, Dohoon Kim, Taesup Moon

llms fine-tuning benchmarks safety

arXiv AI

Jul 22

QuArch: A Benchmark for Evaluating LLM Reasoning in Computer Architecture

arXiv:2510. 22087v2 Announce Type: replace-cross Abstract: The field of computer architecture, which bridges high-level software abstractions and low-level hardware implementations, remains absent from current large language model (LLM) evaluations.

By Shvetank Prakash, Andrew Cheng, Mark Mazumder, Arya Tschand, Varun Gohil, Jeffrey Ma, Jason Yik, Zishen Wan, Jessica Quaye, Elisavet Lydia Alvanaki, Avinash Kumar, Chandrashis Mazumdar, Tuhin Khare, Alexander Ingare, Ikechukwu Uchendu, Radhika Ghosal, Abhishek Tyagi, Chenyu Wang, Andrea Mattia Garavagno, Sarah Gu, Alice Guo, Grace Hur, Luca P. Carloni, Tushar Krishna, Ankita Nayak, Amir Yazdanbakhsh, Vijay Janapa Reddi

llms fine-tuning benchmarks

arXiv AI

Jul 22

MEDIC: Comprehensive Evaluation of Leading Indicators for LLM Safety and Utility in Clinical Applications

arXiv:2409. 07314v3 Announce Type: replace-cross Abstract: While Large Language Models (LLMs) achieve superhuman performance on standardized medical licensing exams, these static benchmarks have become saturated and increasingly disconnected from the functional requirements of clinical workflows.

By Praveenkumar Kanithi, Cl\'ement Christophe, Marco AF Pimentel, Tathagata Raha, Prateek Munjal, Nada Saadi, Hamza A Javed, Svetlana Maslenkova, Nasir Hayat, Ronnie Rajan, Shadab Khan

llms fine-tuning benchmarks safety data-engineering

arXiv AI

Jul 22

Soft-TransFormers for Continual Learning

arXiv:2411. 16073v4 Announce Type: replace-cross Abstract: Inspired by the Well-initialized Lottery Ticket Hypothesis (WLTH), we introduce Soft-TransFormers (Soft-TF), a continual learning framework that adapts a frozen pre-trained Transformer through task-specific soft subnetworks: real-valued multiplicative masks over the query, key, value, and output projections of selected self-attention layers.

By Haeyong Kang, Chang D. Yoo

llms fine-tuning benchmarks

arXiv Machine Learning

Jul 22

Mark, Don't Erase: Token Inoculation for Dual-Use Knowledge in LLMs

arXiv:2607. 18639v1 Announce Type: new Abstract: Safety interventions on dual-use knowledge typically choose between destroying hazardous content (e.

By Seunghyun Lee, Dongyoon Han, Sangdoo Yun

llms fine-tuning benchmarks safety

arXiv AI

Jul 22

Code Division Modulation Layers Against Forgetting and Inference in Continual Gait Identification

arXiv:2607. 19122v1 Announce Type: cross Abstract: Continual learning (CL) has been recently employed in biometric identification systems thanks to its ability to integrate new knowledge within a pre-trained model and to the possibility of reducing the computational cost of training.

By Simone Milani

fine-tuning safety

arXiv AI

Jul 22

Norm or Direction? Decoding Vision Mambas for High-Resolution Vision

arXiv:2607. 18625v1 Announce Type: cross Abstract: Vision Mamba models replace quadratic self-attention with linear complexity selective state space models (SSMs), emerging as efficient visual backbones.

By Jin Yu, Juyoun Park

computer-vision fine-tuning safety

arXiv AI

Jul 22

SFGA: A Statistics-First Gating Architecture with Adjudicative Escalation for Trustworthy SFT Data Procurement

arXiv:2607. 18960v1 Announce Type: cross Abstract: Procuring supervised fine-tuning (SFT) data forces a buyer to decide, before any downstream training, whether a candidate corpus is worth acquiring.

By Arther Tian, Alex Ding, Simon Wu, Aaron Chan

llms fine-tuning benchmarks

arXiv Machine Learning

Jul 22

NSMA: Neuro-Symbolic Manifold Alignment for Generalizable Adaptive Bitrate Streaming under Texture Shift

arXiv:2607. 18845v1 Announce Type: cross Abstract: For decades, ABR has kept two kinds of intelligence apart.

By Zhiqiang He, Zhi Liu

diffusion fine-tuning benchmarks safety

arXiv AI

Jul 22

Beyond Score Prediction: LLM-Based Essay Scoring and Feedback Generation via Reinforcement Learning with Rubric Rewards

arXiv:2607. 19219v1 Announce Type: cross Abstract: Large language models (LLMs) have been widely applied to automated essay scoring (AES) and automated feedback generation (AFG).

By Xuefeng Jin, Jiashuo Zhang, Teng Cao, Bin Yang

llms reinforcement-learning fine-tuning benchmarks

arXiv Machine Learning

Jul 22

Adopting Reinforcement Learning with Verifiable Rewards for Molecular Generation

arXiv:2607. 19044v1 Announce Type: new Abstract: Leveraging large language models (LLMs) for molecular generation has shown remarkable potential in chemical and drug design.

By Mingxuan Ouyang, Hao Lan, Wanyu Lin

llms reinforcement-learning fine-tuning benchmarks

arXiv AI

Jul 22

Physical Self-Supervised Learning: IMU Sensing without Manual Labels

arXiv:2607. 18361v1 Announce Type: cross Abstract: Deep neural networks have become a promising approach for IMU-based sensing, but their scalability is fundamentally limited by costly labeled data and poor robustness to heterogeneous devices, placements, and users.

By Yuyang Leng (Richard), Renyuan Liu (Richard), Shaohan Hu (Richard), Peijun Zhao (Richard), Chun-Fu Chen (Richard), Songqing Chen, Shuochao Yao

diffusion fine-tuning benchmarks

arXiv AI

Jul 22

Now You See the Hate: Adaptive View Retrieval for Hidden Hateful Illusions

arXiv:2607. 19061v1 Announce Type: cross Abstract: Hateful optical illusions expose a serious gap in current multimodal safety systems.

By Qianpu Chen, Derya Soydaner

llms fine-tuning multimodal benchmarks safety

arXiv AI

Jul 22

SciCodePile: A 128GB Corpus and Executable Benchmark for Challenging Scientific Code Generation

arXiv:2607. 19104v1 Announce Type: cross Abstract: Large language models (LLMs) excel at general-purpose code generation, yet how well they handle scientific code remains an open question.

By Weifeng Sun, Ye Fan, Yuchen Chen, Gou Tan, Jieke Shi, Yuan Yidi, Swee Liang Wong, Jonathan Pan, David Lo

llms fine-tuning benchmarks

arXiv AI

Jul 22

Operational Proto-Introspection in Looped Language Models: Process-Quality Taps, Executable Branching, and the Readout-Control Boundary

arXiv:2607. 18553v1 Announce Type: cross Abstract: Can a language model read the quality of ongoing computation, and can an external intervention turn that readout into better outcomes?

By Jan Kirin

llms fine-tuning

arXiv Machine Learning

Jul 22

CircuitKIT : Circuit Discovery, Evaluation, and Application Toolkit for Mechanistic Interpretability

arXiv:2607. 19317v1 Announce Type: new Abstract: Circuit analysis can support not only model explanation but also downstream interventions such as pruning, editing, steering, and selective fine-tuning.

By Pratinav Seth, Hem Gosalia, Aditya Kasliwal, Vinay Kumar Sankarapu

fine-tuning efficiency safety

Get the daily briefing