SAUL把锐度感知与增强拉格朗日结合，给机器遗忘难题开了一剂新药方。
arXiv:2608.16249v1 Announce Type: new Abstract: Machine unlearning in Large Language Models (LLMs) faces a critical trade-off between erasing target k…
SpotOptim优化器新工作论文，十八页篇幅系统介绍方法思路，技术细节与理论分析兼备，值得算法研究者关注。
arXiv:2604.13672v2 Announce Type: replace Abstract: The spotoptim package implements surrogate-model-based optimization of expensive black-box functio…
用检索增强生成驱动大模型，破解复杂约束建模与优化难题，工程实践必读。
arXiv:2608.00015v1 Announce Type: new Abstract: Both optimization modeling and constraint modeling are non-trivial problems requiring deep domain expe…
如何在不确知备选对象价值时，依序挑选最优群体？这篇论文给出新解法。
arXiv:2508.16386v2 Announce Type: replace Abstract: We study the problem of fair cohort selection under uncertainty, motivated by university admission…
基于几何原理的随机优化方法，为大规模语言模型训练效率提供新思路
arXiv:2510.01878v2 Announce Type: replace Abstract: Low-rank gradient optimization for large language models is currently divided into two categories:…
最小范数目标驱动的组聚合方法，为高维数据分组与优化带来全新解法。
arXiv:2606.22917v1 Announce Type: new Abstract: Learning instability is a long-standing problem across machine learning, but it is especially acute in…
在线镜像下降的近似误差竟有隐藏代价，理论推导揭示收敛性新边界，优化算法研究者必读。
arXiv:2511.22283v2 Announce Type: replace Abstract: Online mirror descent (OMD) is a fundamental algorithmic paradigm that underlies many algorithms i…
直达 arXiv 深度学习论文页面，免费获取 Schatten-p 范数分析与 Muon 优化器的对比预印本
arXiv:2606.15268v1 Announce Type: new Abstract: Schatten-$\infty$ based optimizers such as Muon have shown promising empirical performance, but there …
将低秩最优传输问题转化为黎曼流形上的优化，显著提升计算效率与可扩展性，理论突破值得关注。
arXiv:2606.12120v1 Announce Type: new Abstract: Low-rank optimal transport (OT) mitigates the quadratic scaling of classical solvers, yet existing app…
提出贝尔曼-泰勒分数解码方法，解决状态依赖可行动作集下MDP的优化难题，为强化学习提供新视角。
arXiv:2606.10979v1 Announce Type: new Abstract: Many Markov decision processes (MDPs) in operations research have feasible actions that are state depe…
提出软序列策略优化方法，用更平滑的目标函数处理序列决策问题，提升训练稳定性和性能。
arXiv:2602.19327v3 Announce Type: replace-cross Abstract: A significant portion of recent research on Large Language Model (LLM) alignment focuses on …
进化算法在物理知情优化中如何平衡性能与可解释性？这项研究给出了关键需求分析。
arXiv:2605.28164v1 Announce Type: cross Abstract: Evolutionary computation offers a variety of tools to solve complex real-world optimization problems…
强化学习新突破：信任区域与Q学习的巧妙融合，伴随匹配算法提升训练稳定性
arXiv:2605.27079v1 Announce Type: cross Abstract: Off-policy reinforcement learning of pretrained flow policies remains challenging due to the instabi…
约束获取领域现有基准测试存在缺陷，这篇论文呼吁建立更全面、更具挑战性的评估标准，推动算法进步。
arXiv:2605.26279v1 Announce Type: new Abstract: Constraint Acquisition (CA) and related research on the validation and enhancement of Mathematical Pro…
Muon优化器新升级MONA，融合Nesterov加速突破局部极小，提升大模型训练效率
arXiv:2605.26842v1 Announce Type: new Abstract: The Muon optimizer has recently offered a promising alternative to AdamW for large language model trai…
从理解到加速，全方位优化MeanFlow训练方法，为流模型训练效率带来显著提升。
arXiv:2511.19065v2 Announce Type: replace-cross Abstract: MeanFlow promises high-quality generative modeling in few steps, by jointly learning instant…
打破传统训练调度限制，谱优化实现随时中断与继续，灵活高效。
arXiv:2605.23061v1 Announce Type: cross Abstract: Standard neural network training relies on learning-rate schedules tied to a fixed horizon, leading …
Muon优化器新变体AMUSE，实现任意时刻稳定梯度评估，41页论文含25图详解算法改进。
arXiv:2605.22432v1 Announce Type: new Abstract: Modern deep learning commonly relies on AdamW with prescribed learning rate schedules, but recent work…
提出自适应Muon正交化方法，有望优化深度学习训练过程。
arXiv:2605.17806v1 Announce Type: new Abstract: Muon has recently emerged as a competitive alternative to AdamW for large-scale pre-training, with ort…
提出异步线性最小化预言机动量方法，加速大规模组合优化
arXiv:2605.18174v1 Announce Type: new Abstract: Muon has recently emerged as a strong alternative to AdamW for training neural networks, with encourag…