Approaches

Depth-4 search and long outcomes

Judges moves over 25-step continuations, then tries to retain only choices that survive the longer view.

Depth-4 search and long outcomes

Depth-4 distillation

Tests whether a small model can imitate the reference and finds that a cheap exact search does better.

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Lifetime objective

Judge a move by how much longer the game will still last, rather than by how many points it scores right now.

Lifetime objectivefeatured

Compressing a legal planner into a network

Labels every legal move with an expensive public-information planner, then trains a small network to imitate it.

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N-tuple networks and learning from play

Learn what makes a Drop7 board good from small cell patterns and millions of self-play games.

N-tuple networks and learning from play

Expert iteration from the depth-4 search

Learn from the strongest search, play with the result, and repeat. After eight rounds, the learned policy was still less than half as good as its teacher.

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N-tuple networks and learning from play

Learning what a long-lived board looks like

Train a classifier to recognise boards from very long games and use it to guide play. It classified almost perfectly but chose worse moves.

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Value and policy learning

Instead of searching ahead, train a model on past games to judge a board or pick a column, and learn why that kept failing.

Value and policy learning

D4 root-Q clone

Train a tiny fast network to copy how the expensive depth-4 search orders the seven columns.

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Value and policy learning

Phase distillation

Train a fast network to copy a slow but reliable search, using both its chosen column and the values behind that choice.

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