onepass-c4 — a one-pass specialist that plays Connect Four

A one-pass specialist scores a supplied list of options in one forward pass: context bytes in, one score per option out, no generation, no tokenizer. This one plays Connect Four: the board goes in as 44 bytes, the legal columns go in as short strings (column 4), and the highest-scoring column is played. No search, no hand-written rules — the scores are the whole decision.

Play it in your browser: https://precisit.github.io/onepass-web/demo/c4/ (the page's arena pits v2 against v1 or a random player).

The story behind the model: Meet your one-pass AI opponent follows the first attempt, the better teacher and the browser release.

v2 (current)

architecture	the one-pass option scorer from one-pass-specialists (byte embedding → 8 transformer layers, width 256 → option attention head) — the same architecture as v1, larger config
parameters	7.38 M
input	context 44 bytes: 1: or 2: (did the player to move open the game?) + 42 cells, bottom row first, m = mover, t = opponent, . = empty; options column 1 … column 7, 8 bytes each
files	onepass-c4-v2-int8.onnx (7.8 MB, ONNX Runtime dynamic int8 — what the demo loads) · onepass-c4-v2.onnx (fp32, 29.7 MB) · v2/model.safetensors + v2/config.json (toolkit checkpoint)
ONNX inputs	context_ids int32 [1, 44], option_ids int32 [1, 7, 8], option_mask int32 [1, 7]; bytes are UTF-8 + 1, 0 = padding; output logits [1, 7]

How well it plays

Frozen protocol, pre-registered before training: 200 games per match, colours alternate, games start from the empty board, and both players make a uniformly random move 5 % of the time (otherwise two deterministic players replay one game). Score = (wins + draws/2) / games, 95 % Wilson intervals.

opponent	v2 score (checkpoint)	v2 score (int8 file)	v1 score
depth-2 alpha-beta bot	0.905 [0.856–0.938]	0.910	0.03
depth-4 alpha-beta bot	0.915 [0.868–0.946]	0.905	0.03
depth-6 alpha-beta bot	0.893 [0.842–0.928]	0.878	0.02
perfect player (exact solver)	0.475 [0.407–0.544]	—	—
random player	1.000	—	0.885
v1	0.985	—	—

For scale, a perfect player scores 0.890 against the depth-4 bot and 0.925 against the depth-6 bot under the same protocol — its own 5 % random moves cost it the rest. Against the perfect player itself v2 is close to even.

Move level, on a held-out set of 17 325 positions covering every ply (half from strong self-play, half from games between weak and strong players): 98.7 % of v2's moves keep the game-theoretic value of the position (win stays win, draw stays draw); 97.0 % on positions where the choice matters; 93.5 % of its moves are a fastest win / slowest loss. It reads the board: reassigning every stone's owner (column heights kept) drops the non-trivial rate by 49 points.

Browser: the int8 file runs in ONNX Runtime Web; ~20 ms per decision on wasm and ~9 ms on WebNN in headless Chromium on an M1 Max (v1 on the same machine: 10.8 ms on wasm).

How it was trained

Full recipe, protocol and every measurement: examples/c4 in one-pass-specialists.

• Labels: every legal column of every position scored exactly (win/draw/loss and distance), by connect-four-ai (MIT), verified before use against Pascal Pons' public test sets (6 000 positions), the UCI Connect-4 8-ply database (67 557 positions) and an independent solver — zero disagreements.
• Positions: 41.6 M unique positions from TonyCWang/ConnectFour (MIT; solver self-play at varied temperature, every column scored — re-solved on a 20 k sample with zero disagreements) plus 513 k positions from our own games between weak and strong players.
• Target: listwise cross-entropy over the legal columns, towards a softmax of the exact scores in which the win/draw/loss class dominates and distance breaks ties.
• Schedule: 18 000 steps × 1 024 positions (≈ 18 M samples), ply-balanced sampling, random horizontal mirroring; about two hours on one Apple M5 Pro.

Limitations

• Not perfect: 1.3 % of its moves give away the game-theoretic value, and it loses to a perfect player about as often as it wins under the noisy protocol.
• Its choice between near-equal columns is not mirror-symmetric (~77 % consistency on mirrored boards).
• The int8 file picks a different column than the fp32 one on 1.9 % of positions (mostly near-ties; its value-preserving rate is the same, 98.75 %); its measured strength is shown separately above.

v1 (previous, kept for comparison)

onepass-c4-8x24.onnx + model.safetensors (706 k parameters, 224 context bytes, 8 × 24 option bytes): trained on ~80 k endgame positions only. It read the position from a move-history line rather than the board, and scores 0.02–0.03 against the search bots above. It stays here because the demo's arena plays it against v2.

License and credits

MIT. Labels by connect-four-ai (MIT, Benjamin Rall); positions from TonyCWang/ConnectFour (MIT); evaluation positions from the UCI Connect-4 database (John Tromp, CC BY 4.0) and Pascal Pons' test sets were used only to verify the labeller. Architecture adapted in one-pass-specialists from Cua-S1 / jevlike (MIT).