突破LLM持续微调遗忘瓶颈，ReCoLoRA以频谱感知递归合并实现任务序列高效学习。
arXiv:2607.07719v1 Announce Type: new Abstract: Parameter-efficient fine-tuning adapts a large language model to one task cheaply, but across a task s…
面向低资源语言罗马尼亚语的多模态指令微调，用参数高效方法实现视觉语言模型适配，填补非英语VLM研究空白。
arXiv:2512.14926v2 Announce Type: replace-cross Abstract: Focusing on low-resource languages is an essential step toward democratizing generative AI. …
新研究前沿：针对大语言模型的参数高效微调，如何通过经验隐私审计量化个体记忆泄露风险，提升数据安全。
arXiv:2606.10481v1 Announce Type: cross Abstract: Parameter-efficient fine-tuning of large language models (LLMs) can exhibit problematic memorization…
探索用LoRA和NEFTune方法高效微调DeepSeek-R1-8B，降低资源消耗同时提升性能。
arXiv:2606.10392v1 Announce Type: new Abstract: Financial named-entity recognition (NER) is essential for translating unstructured financial reports a…
提出动态核心空间合并方法，大幅降低混合LoRA专家模型的内存占用与计算开销
arXiv:2603.00573v2 Announce Type: replace Abstract: Large language models (LLMs) achieve remarkable performance on diverse downstream and domain-speci…
NaRA提出噪声感知LoRA，显著提升扩散大语言模型参数高效微调效果
arXiv:2605.29716v1 Announce Type: new Abstract: Diffusion Large Language Models (dLLMs) have emerged as a promising non-autoregressive generative para…
LoRA新思路：在Fisher子空间中引导初始化，提升微调性能与稳定性。
arXiv:2605.01046v3 Announce Type: replace Abstract: LoRA adapts large language models (LLMs) by restricting updates to low-rank subspaces of pre-train…
arXiv最新论文提出稀疏正交参数调优方法，仅需微调少量正交参数即可有效缓解持续学习中的灾难性遗忘。
arXiv:2411.02813v3 Announce Type: replace Abstract: Continual learning methods based on pre-trained models (PTM) have recently gained attention which …
通过调整学习率，简单LoRA即可媲美复杂微调方法，揭示被忽视的关键因素。
arXiv:2602.04998v2 Announce Type: replace Abstract: Low-Rank Adaptation (LoRA) is the prevailing approach for efficient large language model (LLM) fin…
提出TiTok方法，通过对比学习转移Token级知识，让LoRA参数可在不同骨干模型间移植。
arXiv:2510.04682v3 Announce Type: replace-cross Abstract: Large Language Models (LLMs) are widely applied in real world scenarios, yet fine-tuning the…
多任务微调新方法，用连续提示优化实现参数高效的大模型适配，减少数据需求。
arXiv:2605.14055v1 Announce Type: cross Abstract: Parameter-Efficient Fine-Tuning (PEFT) is widely used for adapting Large Language Models (LLMs) for …
策略性过参数化提升低秩适应泛化能力，为参数高效微调提供新理论。
arXiv:2605.16470v1 Announce Type: new Abstract: Adapting large language models (LLMs) to downstream tasks via full fine-tuning is increasingly impract…
提出GLT-PEFT新范式，用门控李-塔克分解实现参数高效微调，专攻阿尔茨海默病诊断中的海马分割预训练。
arXiv:2605.16769v1 Announce Type: new Abstract: Parameter-efficient fine-tuning (PEFT) has emerged as a promising paradigm for adapting pretrained mod…
新方法LoCO通过低秩组合正交微调，在保持预训练几何结构的同时实现高效参数调整，为NLP和计算机视觉大模型适配提供新思路。
arXiv:2605.15916v1 Announce Type: cross Abstract: Parameter-efficient fine-tuning (PEFT) has emerged as an critical technique for adapting large-scale…
LoRA的等距瓶颈终于被打破！GPart用单次随机投影实现端到端等距微调，仅需存储d+1个值，在NLU、CV、数学推理上达到SOTA，参数效率极致。
arXiv:2605.14841v1 Announce Type: cross Abstract: Low-rank adaptation (LoRA) has become the dominant paradigm for parameter-efficient fine-tuning (PEF…