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提出 CLIK，一个上下文条件化的参数生成器，通过单次前向传播将上下文映射为 LoRA 参数，把知识内化到模型权重中，无需测试时梯度更新，在文档问答和多轮对话上优于现有超网络方法。
提出一种层级低秩重参数化方法，将PEFT方法的可训练参数重组为近似方阵并递归进行低秩分解，可在压缩约一个数量级参数的同时保持甚至提升LoRA/DoRA在常识推理与数学推理任务上的性能。
该论文提出 Fiber Regulation Theory (FRT) 与 Fiber Acquisition Theory (FAT) 两个理论框架，刻画受保护的功能修复与可训练结构的形成/续期动力学，并在 Qwen2.5-32B 上通过受保护头修复与固定容量 LoRA 续期验证联合策略优于单独策略。
MorphAct 提出一种上下文参数化框架，通过两个耦合的低秩生成器动态调整机器人策略参数，缓解不同交互上下文中相互冲突的参数更新，在 LIBERO、Meta-World 及真实机器人实验中相比多个 VLA 骨干网络最高提升 16.30%。
论文提出 JIVEAdapter，一种受统计学 Joint and Individual Variation Explained 启发的多任务加性低秩适配器，将权重更新分解为跨任务共享的 Joint 结构与任务专属且近正交的 Individual 结构，并在 GLUE/SuperGLUE 上与 DeBERTaV3-base 取得竞争力表现。
This paper introduces ℓp-LoRA, a principled method for automatic rank allocation in low-rank adaptation using ℓp regularization, demonstrating competitive performance on NLP tasks.
The paper introduces NSFT, a fine-grained parameter-efficient fine-tuning framework for MoE LLMs that refines adaptation from experts to sub-experts, demonstrating improved performance with fewer trainable parameters.
The paper proposes ChainDoRA, a parameter-efficient fine-tuning method for large language models that combines Tensor-Train factorization with weight-decomposed low-rank adaptation, achieving a 90.62% reduction in trainable parameters and improved accuracy over LoRA and DoRA.
This paper proposes a three-stage pipeline for accented conversational ASR that improves entity and disfluency recall, achieving 80–85% entity recall and outperforming baseline systems with fewer parameters.
This paper presents Iso-LoRA, an optimizer that enhances LoRA by promoting even energy distribution across singular directions through spectral descent on tangent-space perturbations, improving effective rank and downstream performance in language model adaptation.
TaRA is a training-aware initialization method for Low-Rank Adaptation (LoRA) that improves gradient fidelity, leading to better fine-tuning performance for large language models across various tasks.
Normalized Low-Rank Adaptation (NoRA) stabilizes LoRA training by normalizing down-projection matrices, accelerating convergence and improving performance without extra parameters or inference cost.
This paper identifies a structural failure mode in token-level credit assignment for LLM reinforcement learning when using LoRA, where intrinsic signals degenerate. It proposes Adapter-Residual Credit Assignment (ARCA), which derives token salience from adapter hidden-state residuals and remains competitive with baselines.
PreFT proposes applying adapters only to prefill tokens, discarding them during decode, which increases throughput for multi-adapter serving with minimal performance loss.