# Study of the Proper NNUE Dataset

arXiv: https://arxiv.org/abs/2412.17948 · HTML: https://mlsanity.com/paper/2412.17948/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 1 of its 20 references that are in the map.
- First posted on arXiv 23 Dec 2024.

## Builds on (most cited first, 1 of 1)

- [Neural Networks for Chess](https://mlsanity.com/paper/2209.01506/) (2022)

## Abstract (arXiv)

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.