{
 "about": "Recorded training curves of the four models shipped on woodpip.com. Training loss: cross-entropy between the teacher's target distribution and the model's softmax over the legal moves, on one training batch, logged every 100 steps ('loss') with an exponential moving average ('loss_ema', 0.98 per step). It includes the teacher targets' own entropy, which was not logged, so its absolute value is not a distance to the teacher. Validation: a fixed subset of the validation set every 2,000 steps (Four in a Row stage 1: every 1,000), used only to select the checkpoint; the reported results come from the separate evaluation set, once per model. 'train_loss_ema' at a validation point is the training-loss average at that step, not a validation loss (validation loss was not logged).",
 "metrics": {
  "mean_regret": "Five Dice and Backgammon: the teacher's value of its best move minus the value of the model's move, averaged over validation decisions (Five Dice: points of expected final score; Backgammon: equity, points per game). Lower is better; the checkpoint selection maximizes its negative.",
  "value_preserving": "Four in a Row and Reversi: the share of validation positions where the model's move keeps the win/draw/loss sign of the best move's value (Four in a Row: the exact solver's; Reversi: the teacher's label, exact near the end and a fixed-depth estimate earlier). Higher is better; the checkpoint selection maximizes it.",
  "best_move": "Share of validation decisions where the model's move is one of the teacher's best moves.",
  "exact_top1": "Four in a Row: share where the model's move is the solver's best move."
 },
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