CAMFT is a Conflict‑Aware Mergeable Fine‑Tuning method designed to make task adaptation efficient and merge‑aware for large language models. Unlike existing approaches that only resolve parameter conflicts after fine‑tuning, CAMFT shapes mergeability during training by guiding each task to update sparse coordinates with lower cross‑task conflict. Experiments show that CAMFT outperforms standard fine‑tuning baselines in multi‑task merging scenarios.
By Jingang Zhou, Haiyang Guo, Yuan Ma, Han Zhu, Xu-Yao Zhang
As large language models (LLMs) scale rapidly, dense full-parameter adaptation becomes increasingly expensive, motivating sparse and modular architectures such as Mixture-of-Experts (MoE) models. This...
arXiv:2609.25655v1 Announce Type: new
Abstract: As large language models (LLMs) scale rapidly, dense full-parameter adaptation becomes increasingly expensive, motivating sparse and modular architectu...
By Zhentao Tan, Chang Liu, Yao Liu, Yue Wu, Jieping Ye
The paper proposes Layer-Informed Fine-Tuning (LIFT), a method that identifies and updates only the most functionally critical layers of large language models (LLMs) using a bottleneck identification mechanism based on sensitivity analysis. By focusing on layers that handle conceptualization, reasoning, and textualization, LIFT aims to accelerate training and enhance performance on reasoning tasks. Experiments demonstrate that this selective fine-tuning approach both speeds up the training process and yields significant performance gains.
By Junning Shao, Siwei Wang, Zhixuan Fang
arXiv:2607. 03932v1 Announce Type: cross Abstract: LLMs can be conveniently adapted to a diverse set of tasks, e.
By Shubhadip Nag, Srinjoy Das, Agniva Saha, Anushree Ghosh, Soumi Das, Tarun Kumar, Suparna Bhattacharya, Sourangshu Bhattacharya
The paper introduces Align‑LoRA, a unified LoRA framework for multi‑task learning that replaces complex, isolated adapter designs with a single‑adapter model enhanced by a higher rank and an explicit alignment loss. It demonstrates that a router‑free, multi‑head model with high inter‑head redundancy can outperform more elaborate baselines, and that a unified LoRA can achieve competitive performance while enabling weight merging and zero inference latency. Extensive experiments and theoretical analysis confirm that Align‑LoRA surpasses prevailing approaches, offering a simpler, production‑friendly paradigm for parameter‑efficient fine‑tuning of large language models.
By Jinda Liu, Yi Chang, Yuan Wu
Prompt2Skill is an unsupervised framework that constructs skills for Large Language Models directly from natural‑language task descriptions. It automatically derives task specifications, discovers or synthesizes datasets, and refines the skill through a reflective editing loop. In experiments across question answering, reading comprehension, spreadsheet manipulation, and mathematical reasoning, Prompt2Skill outperforms direct prompting, improving performance by an average of 10.8 points on both open‑source and frontier models.
By Bo Ni, Li Li, Ryan A. Rossi, Franck Dernoncourt, Tyler Derr
arXiv:2607. 28657v1 Announce Type: new Abstract: Large Language Models (LLMs) often require carefully crafted prompts to unlock their full potential, which can be a barrier for non-expert users.
By Oliver Savolainen, Emanuele Bastianelli, Hosein Azarbonyad
arXiv:2607. 06160v1 Announce Type: cross Abstract: Synthesizing long-context supervised fine-tuning (SFT) data is a scalable way to enhance the long-context understanding of large language models (LLMs), yet existing approaches share three limitations: narrow task coverage, insufficient instruction difficulty, and a lack of faithfulness supervision.
By Chenhao Yuan, Yinhao Xu, Shuwen Xu, Xizhi Yang, Jiaxiang Liu, Chenxi Zhou, Shaoping Huang, Haolin Ren, Pengfei Cao, Jun Zhao, Kang Liu
arXiv:2608.30426v1 Announce Type: new
Abstract: Current dialogue systems struggle with dynamic information retrieval, often leading to hallucinations and lower response accuracy. We address this by a...
By Markel Ferro, Oier Lopez de Lacalle
arXiv:2606. 24841v1 Announce Type: new Abstract: Prompt-based learning has emerged as a dominant paradigm in natural language processing.
By Ahmad Pouramini, Hesham Faili
The paper introduces a family of adapters that enhance language model reasoning by adding selective state-space control at token and context levels. The token-level MaLoRA makes the adapter’s scaling factor dynamic and recurrent, improving over static low‑rank adaptation. The context-level MaRA tracks cross‑segment reasoning state and retrieves relevant segments, outperforming an eight‑billion‑parameter dense retriever and boosting reasoning accuracy by an average of +6.4 F1 over LoRA.
By Atahan Dokme, Larry Heck