ChessLab is a chess learner that starts from random weights and is told nothing about chess but the rules. It plays itself, trains on its own games, and a candidate becomes the champion only by beating the old one over enough games to prove it. This is the lab's own instrument panel, published from the training machine every few seconds — a copy pushed out, never a window in.
Now
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Cycle ends
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Hours per generation
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measuring
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Against its own ancestors and fixed rungs only. Not a distance to Stockfish.
Reference Elo
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Selected game
-| Move | Guess → search | Guess | Search | Change |
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One position from a game the observer follows. The network guesses first, the search corrects the guess, and the corrected share is what the next training step teaches the network; the gap between the two columns is the whole learning signal. Candidates the search never visited and the network gave under 1% are folded into the last row.
Opponents
nothing measured yet| Opponent | Score | Games · W-D-L | Trend | Series |
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Ancestor anchors
-| Ancestor | Score | Lower 95 | Elo ahead | Record | Read by | Age | Earlier readings |
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How far ahead of a frozen ancestor the champion that last played it was, from a 200-game match apiece; newest ancestor first, and a reading is only a current standing if the champion that took it is the one we have now. An ancestor never moves, so a point that rises between two readings of it is us moving. Elo is the log-odds of the score and compresses at the top: 96% and 98% sit 120 Elo apart on four games' difference.
Piece values
-| Piece | Value | x pawn | of queen | Change |
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What the value head itself thinks a piece is worth, with the material lesson switched off so the network answers alone. Nobody told it a queen outranks a pawn; whatever ordering appears, it found.
Forced mates
-| Probe | Solved | Positions | Target mass | Change |
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Fixed positions from this run's own self-play where mate is forced. The rules decide these, so unlike the gate and the anchors they cannot both be wrong at once. Mate in 1 is settled by a root scan and should stay at 100% -- a fall means a regression. Mate in 2 needs real search, and is the number to watch. Target mass is what the stored policy would teach: a search can play the mate and still record a target saying it has probability zero.
Is the progress real?
-| Instrument | Now | Trend | Evidence |
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One row per instrument, each judged by its own measured noise floor, so a step inside the noise is "steady". The fixed opponents cannot move under the measurement; the gate and the probes compare the learner with itself and can agree while it stands still. Rungs the ladder no longer plays are folded, with their last reading.
Generations
-| Cycle | Gen | Gate | Games | Positions | Pos/h | Minutes | Passes | Policy KL | Held-out KL | Held-out CE |
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One row per cycle; a generation only advances when the gate promotes, so several cycles share one. Policy KL is how far the network's guess sits from the search's answer, on the positions it trained on and on held-out games it never saw; held-out value CE above ln 3 = 1.099 would mean the value head generalises nothing. The last five cycles are rows, the rest are the lines.