提出稳定棋局数据集生成方法，提升棋类评估模型性能
Study of the Proper NNUE Dataset
- 基于静止局面筛选构建无战术波动数据集
- 实测显著提升棋类引擎对复杂局面的评估能力
- 方法可复现，适合需高质量训练数据的研究者
NNUE（高效可更新神经网络）已彻底改变棋类引擎开发，几乎所有顶级引擎均采用NNUE模型以保持竞争力。然而，在复杂领域如国际象棋中，构建高质量数据集仍是关键挑战，尤其在需要兼顾战术与战略评估的情况下。现有数据集构建方法缺乏系统性理解与文档支持。本文提出一种算法，用于生成并过滤由“静止”局面组成的数据集，这些局面具有稳定性且不受战术波动干扰。该方法提供了一套清晰、可复现的构建流程，适用于多种评估函数。实验验证表明，使用该数据集训练的引擎在多项测试中表现显著提升，证实了方法的有效性。
原文摘要 · Abstract (English)
NNUE (Efficiently Updatable Neural Networks) has revolutionized chess engine development, with nearly all top engines adopting NNUE models to maintain competitive performance. A key challenge in NNUE training is the creation of high-quality datasets, particularly in complex domains like chess, where tactical and strategic evaluations are essential. However, methods for constructing effective datasets remain poorly understood and under-documented. In this paper, we propose an algorithm for generating and filtering datasets composed of "quiet" positions that are stable and free from tactical volatility. Our approach provides a clear methodology for dataset creation, which can be replicated and generalized across various evaluation functions. Testing demonstrates significant improvements in engine performance, confirming the effectiveness of our method.
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