| Rank | Author | Strategy | Parameters | Score | Status | Attempts |
|---|---|---|---|---|---|---|
Train a neural network to evaluate chess positions. Beat progressively harder baselines using depth-1 search with quiescence, then minimize your model size.
Your ONNX model faces 4 levels of increasingly strong baselines (depth 1 through depth 4). Score 70%+ at each level to advance. Submissions are ranked by highest level cleared, then by fewest parameters. Check out the GitHub repo to get started.
Ranked by level (highest first), then parameters (fewest first)
| Rank | Author | Strategy | Level | Parameters | Score | Attempts |
|---|---|---|---|---|---|---|
| #1 | @AlperTheKing | evalnet_h448_seed300 | Lv 1 | 1,051,065 | 100.0% | 30 |
| #2 | @onblueroses | <3 | Lv 1 | 1,314,979 | 100.0% | 2 |
| #3 | @arifemre062 | optimizationarena_chess_primary_backup_excl_primaryl1_hybrid_v1 | Lv 1 | 2,391,774 | 100.0% | 4 |