Abstract
Recent years have witnessed significant progress in reinforcement learning, especially with Zero-like paradigms, which have greatly boosted the generalization and reasoning abilities of large-scale language models. Nevertheless, existing frameworks are often plagued by high implementation complexity and poor reproducibility. To tackle these challenges, we present AlphaZero-Edu, a lightweight, education-focused implementation built upon the mathematical framework of AlphaZero. It boasts a modular architecture that disentangles key components, enabling transparent visualization of the algorithmic processes. Additionally, it is optimized for resource-efficient training on a single NVIDIA RTX 3090 GPU and features highly parallelized self-play data generation, achieving a 3.2-fold speedup with 8 processes. In Gomoku matches, the framework has demonstrated exceptional performance, achieving a consistently high win rate against human opponents. AlphaZero-Edu has been open-sourced at this https URL, providing an accessible and practical benchmark for both academic research and industrial applications.
Abstract (translated)
近年来，强化学习领域取得了显著进展，特别是在类似“从零开始”的范式方面，这些范式极大地提高了大规模语言模型的泛化能力和推理能力。然而，现有的框架常常受到实施复杂性和可重复性差的困扰。为了解决这些问题，我们提出了AlphaZero-Edu，这是一个基于AlphaZero数学框架构建的轻量级、教育导向的实现方案。它具有模块化的架构，能够分离关键组件，并使算法过程透明化可视化。 此外，AlphaZero-Edu经过优化，在单个NVIDIA RTX 3090 GPU上进行资源高效的训练，并且自博弈数据生成高度并行化，使用8个进程可实现速度提高3.2倍。在五子棋比赛中，该框架表现出色，持续以高胜率战胜人类对手。 AlphaZero-Edu已开源，可在提供的网址（[这里](https://github.com/alphazeroedu)）访问，为学术研究和工业应用提供了一个易于使用且实用的基准测试平台。
URL
https://arxiv.org/abs/2504.14636
