Recent years have witnessed significant progress in reinforcement learning,especially with Zero-like paradigms, which have greatly boosted thegeneralization and reasoning abilities of large-scale language models.Nevertheless, existing frameworks are often plagued by high implementationcomplexity and poor reproducibility. To tackle these challenges, we presentAlphaZero-Edu, a lightweight, education-focused implementation built upon themathematical framework of AlphaZero. It boasts a modular architecture thatdisentangles key components, enabling transparent visualization of thealgorithmic processes. Additionally, it is optimized for resource-efficienttraining on a single NVIDIA RTX 3090 GPU and features highly parallelizedself-play data generation, achieving a 3.2-fold speedup with 8 processes. InGomoku matches, the framework has demonstrated exceptional performance,achieving a consistently high win rate against human opponents. AlphaZero-Eduhas been open-sourced at https://github.com/StarLight1212/AlphaZero_Edu,providing an accessible and practical benchmark for both academic research andindustrial applications.