Approaches

Tree search

Instead of examining every column to a fixed depth, grow the look-ahead only where it looks promising, guided by quick simulated playouts.

Let a small learned board evaluator decide where a deeper search should spend its time, while keeping the exact search as a safety net.

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

Give every cell a learned description and predict the game's remaining lifetime. Good predictions still did not produce good choices.

Judge the board at the moment just before the next disc is dealt, so the value never has to be split seven ways.