标题： 粒子物理中风味问题的强化学习技术 显示英文标题

标题： Reinforcement Learning techniques for the flavor problem in particle physics

摘要： 这篇简短的综述讨论了强化学习(RL)技术在粒子物理中味问题的最新应用。传统方法在处理费米子质量与混合时通常依赖于基于水平对称性的标准模型扩展，但可能的模型范围极为广阔，使得系统性探索变得不可行。最近的研究表明，强化学习可以通过构建再现已观测到的夸克和轻子可观测量的模型来高效地探索这一范围。这些方法表明，强化学习不仅能够重新发现文献中已提出的模型，还能发现新的、具有现象学合理性的解决方案。 显示英文摘要

摘要： This short review discusses recent applications of Reinforcement Learning (RL) techniques to the flavor problem in particle physics. Traditional approaches to fermion masses and mixing often rely on extensions of the Standard Model based on horizontal symmetries, but the vast landscape of possible models makes systematic exploration infeasible. Recent works have shown that RL can efficiently navigate this landscape by constructing models that reproduce observed quark and lepton observables. These approaches demonstrate that RL not only rediscovers models already proposed in the literature but also uncovers new, phenomenologically acceptable solutions.