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.

rejectedreproduced

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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Oracle and curriculum research

Let a planner cheat (show it the hidden numbers and the discs still to come) then try to teach a player who cannot cheat.

Oracle and curriculum researchfeatured

Oracle distillation

Show a network thousands of positions labelled with the column a future-reading planner chose, and see whether it can guess that column from the screen alone.

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Oracle and curriculum research

Board topology

Ask whether a player can recognise, from board shape alone, the positions a future-reading planner creates.

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Oracle and curriculum research

Oracle DAgger

Copy the future-reading teacher's moves, then let the copy play and ask the teacher what it should have done in the positions the copy reached.

rejected

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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