提出PLoRA方法，实现大语言模型的高效并发LoRA训练，显著提升微调效率与资源利用率。
arXiv:2508.02932v2 Announce Type: replace Abstract: Low-Rank Adaptation (LoRA) has gained popularity as a fine-tuning approach for Large Language Mode…
扩散与流模型的黑盒对齐新方法，信任区域噪声搜索带来高效微调
arXiv:2603.14504v2 Announce Type: replace-cross Abstract: Optimizing the noise samples of diffusion and flow models is an increasingly popular approac…
大模型安全新方案：通过神经元选择性调优NeST精准提升LLM安全性，不牺牲性能。
arXiv:2602.16835v2 Announce Type: replace-cross Abstract: Safety alignment is essential for the responsible deployment of Large Language Models (LLMs)…
从基因隐喻到模型微调，揭秘如何通过语言特定神经元定向提升大模型多语言性能
arXiv:2602.00945v2 Announce Type: replace-cross Abstract: LLMs are multilingual by training, yet their lingua franca is often English, reflecting Engl…
用LoRA技术让大模型直接理解音频，高效内化跨模态能力，开源研究新突破。
arXiv:2606.11033v1 Announce Type: cross Abstract: Recent efforts to extend large language models (LLMs) to speech inputs typically rely on cascaded AS…
通过扩大训练时对抗攻击规模，提出防御恶意微调的新范式，为AI模型安全提供可扩展方案。
arXiv:2606.07970v1 Announce Type: new Abstract: Current open-weight large language models (LLMs) are prone to malicious finetuning attacks, which coul…
最新研究揭示大模型“遗忘”机制：事实显著性与微调如何双重影响模型知识删除的效果。
arXiv:2602.19612v5 Announce Type: replace Abstract: Machine Unlearning (MU) enables Large Language Models (LLMs) to remove unsafe or outdated informat…
将LoRA重新定义为知识记忆的实证研究，揭示其存储与检索机制，为微调提供新视角。
arXiv:2603.01097v3 Announce Type: replace Abstract: Continuous knowledge updating for pre-trained large language models (LLMs) is increasingly necessa…
专为大模型设备端微调设计的内存高效结构化反向传播，突破移动设备6-12GB内存限制，实现隐私保护与性能平衡。
arXiv:2602.13069v2 Announce Type: replace Abstract: On-device fine-tuning enables privacy-preserving personalization of large language models, but mob…
把后训练循环从样本空间移到权重空间，用高斯扰动采样+奖励排序集成专家，开辟LLM微调新范式。
arXiv:2605.31494v1 Announce Type: cross Abstract: Post-training of language models is commonly framed as a sample-score-update loop implemented by gra…
提出可学习的零阶优化器，无需梯度即可高效微调大模型，大幅降低内存开销。
arXiv:2510.00419v2 Announce Type: replace Abstract: Zeroth-order optimizers have recently emerged as an attractive approach for fine-tuning large lang…
最新研究揭示数据选择策略如何影响大模型微调的长期表现与鲁棒性。
arXiv:2605.30537v1 Announce Type: new Abstract: Data selection is increasingly used to reduce the cost of large language model (LLM) fine-tuning, with…
用语言模型引导贝叶斯优化，高效搜索LoRA超参数，ICML 2026论文带来微调新思路。
arXiv:2602.11171v2 Announce Type: replace-cross Abstract: Fine-tuning Large Language Models (LLMs) with Low-Rank Adaptation (LoRA) offers a resource-e…
动态调整数据与模型匹配度，让知识蒸馏更贴合学生能力，提升推理模型微调效率。
arXiv:2605.29229v1 Announce Type: new Abstract: Reasoning distillation transfers complex reasoning abilities from large language models (LLMs) to smal…
揭秘LoRA微调大模型时如何“记住”信息，提出参数化记忆定律，深入理论分析注意力机制。
arXiv:2605.30260v1 Announce Type: cross Abstract: Large Language Models (LLMs) must continuously learn and update knowledge to remain effective in dyn…
提出On-Policy Replay方法，解决持续监督微调中的灾难性遗忘问题，为LLM高效增量学习提供新思路。
arXiv:2605.29495v1 Announce Type: new Abstract: Continual supervised fine-tuning (SFT) is the de facto recipe for adapting large language models (LLMs…
零阶微调本质是推理负载，颠覆大模型微调的计算认知
arXiv:2605.28760v1 Announce Type: new Abstract: Zeroth-order (ZO) fine-tuning is attractive for large language models because it replaces backpropagat…
LoRA融合能否让云端与边缘协同处理跨域任务？这篇最新论文给出了技术评估与框架分析。
arXiv:2605.23913v1 Announce Type: cross Abstract: Cloud-hosted large language models (LLMs) commonly rely on LoRA for domain adaptation, yet domain da…
联邦学习遇上LoRA新框架，专治大模型在异构设备上协作微调的痛点，方案灵活高效。
arXiv:2501.19389v4 Announce Type: replace Abstract: Fine-tuning large language models (LLMs) on resource-constrained clients remains a challenging pro…
创新激活指导的零阶优化方法，大幅提升大模型微调效率。
arXiv:2601.17261v4 Announce Type: replace Abstract: Zeroth-Order (ZO) optimization has emerged as a promising solution for fine-tuning LLMs under stri…