Study Finds Neural Chess Engine Overrides Its Own Correct Solutions Due to Learned Safety Preferences
Researchers analyzing Leela Chess Zero discovered that the neural network's policy layers systematically override correct puzzle solutions—including immediate checkmates—that appear in intermediate layers, a phenomenon they call "forgotten puzzles." The look-ahead algorithm itself functions normally, with future moves properly represented and causally important, but late-layer safety preferences prioritize conservative play over aggressive winning moves. This finding demonstrates that possessing an internal algorithm does not guarantee using it, with implications for understanding how neural networks make decisions.
A new arXiv study of Leela Chess Zero, the strongest neural chess engine, reveals a surprising disconnect between the algorithms neural networks learn and the decisions they actually make. Using an extended logit lens analysis, researchers found that correct puzzle solutions—including immediate checkmates—frequently appear in intermediate network layers but are systematically suppressed in the final move-selection output. The researchers confirmed the look-ahead algorithm itself works correctly: future moves are properly represented, causally important for predictions, and linearly decodable. Instead, they identified that later network layers increasingly shift toward prioritizing safe, conservative play over aggressive winning strategies. By steering the model away from these safety preferences, the team recovered 61.7% of the overridden solutions, providing causal evidence that learned safety priors, rather than algorithmic failure, drive the override behavior.
What's missing
The study does not discuss whether this safety-override behavior is intentional (e.g., a desirable emergent property for robust play) or unintended, nor does it explore whether similar phenomena occur in other game-playing neural networks or domains beyond chess.
What different sources said
- arXiv cs.AICenter
The Algorithm Is Not the Behavior: Learned Priors Override Look-Ahead in a Chess-Playing Neural Network
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