The paper presents a study on fine‑tuning open‑source instruction‑tuned language models for a Linear Control Systems course using LoRA. A dataset of 360 system‑user‑assistant conversations was created, and LoRA was applied to Qwen2.5‑3B‑Instruct and Qwen2.5‑7B‑Instruct with ranks r=4, 8, and 16. Evaluation with ROUGE, BERTScore, and structured‑output metrics showed that LoRA improved similarity and stability, with the 7B‑r16 model achieving the highest scores and r=8 offering a good trade‑off between performance and parameter efficiency.
By Shaowen Lu, Chengxu Liu, Ping Zhou, Tao Yang
The paper introduces READ, a method for composing low‑rank adapters (LoRA) in large language models. By rewriting each adapter into a balanced canonical form and enforcing a one‑directional coupling, READ allows new skills to read but never write into the output subspaces of existing skills, eliminating interference. Experiments on four benchmark suites and two model families show that READ consistently outperforms existing baselines, improving SuperGLUE scores by over twenty points and domain suite scores by more than seven points.
By Zeyan Li, Panqi Yang, Qirong Guo, Shengda Zhuo, SIyuan Qiu, Hu Xu, Chun Li, Jianfeng Xu
arXiv:2609.15982v1 Announce Type: cross
Abstract: Skills extend an LLM agent beyond its parametric knowledge, and the gain they promise rests on picking the right one. Deployed harnesses route by pre...
By Ruishuo Chen, Xun Wang, Yu Chen, Zhuoran Li, Longbo Huang
arXiv:2605.07111v3 Announce Type: replace-cross
Abstract: Recent literature on fine-tuning Large Language Models highlights a fundamental debate. While Full Fine-Tuning (FFT) provides greater represe...
By Haozhan Tang, Xiuqi Zhu, Xinyin Zhang, Boxun Li, Virginia Smith, Kevin Kuo
arXiv:2606. 16769v1 Announce Type: new Abstract: Agent skills are commonly distributed as SKILL.
By Tianyi Zhang, Zhonghao Qi
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