Understanding the learned look-ahead behavior of chess neural networks
Keywords: model behavior attribution, look-ahead planning, mechanistic interpretability
TL;DR: Chess neural networks exhibit learned look-ahead up to 7 moves and consider multiple move sequences, revealing emergent look-ahead capabilities.
Abstract: We investigate the look-ahead capabilities of chess-playing neural networks, specifically focusing on the Leela Chess Zero policy network. We build on the work of Jenner et al. by analyzing the model's ability to consider future moves and alternative sequences beyond the immediate next move. Our findings reveal that the network's look-ahead behavior is highly context-dependent, varying significantly based on the specific chess position. We demonstrate that the model can process information about board states up to seven moves ahead, utilizing similar internal mechanisms across different future time steps. Additionally, we provide evidence that the network considers multiple possible move sequences rather than focusing on a single line of play. These results offer new insights into the emergence of sophisticated look-ahead capabilities in neural networks trained on strategic tasks, contributing to our understanding of AI reasoning in complex domains. Our work also showcases the effectiveness of interpretability techniques in uncovering cognitive-like processes in artificial intelligence systems.
Primary Area: interpretability and explainable AI
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Submission Number: 11823
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