Sibling advantage

Learn the difference between the columns available right now, not the value of each one, by continuing every column through an identical future so nothing but the choice itself can explain the gap.

The intuition

A policy never needs an absolute number. It needs an ordering. So this approach throws the absolute number away and learns the ordering directly.

The trick that makes that possible is common random numbers. Consider one position with seven legal columns. Drop into column 1 and play on for a while; drop into column 2 from the same starting board and play on for the same while. If the two continuations used different random discs and different gray reveals, the difference in their outcomes is mostly luck. If they use the same tape of future discs and reveals, the luck cancels, and what is left is attributable to the choice.

That design also answers the objection that defeated most of this family. The data is not "the move that was played and its outcome"; it is every legal move and its outcome, at the same position. There is no sibling extrapolation by construction.

How it works

1. Harvest roots. Public positions are taken from games played by the reference policy.
2. Expand every sibling. At each root, every legal column is played, giving up to seven successor boards from one starting board.
3. Continue on aligned tapes. Each successor is rolled forward on exactly the same public continuation tapes as its siblings — 24 of them, each running up to 60 moves — so the difficulty of the source position cannot leak into any one sibling's target.
4. Label relatively. Each sibling's target is its return measured against its siblings at the same root, not against boards from elsewhere in the corpus.
5. Fit with grouped splits. Training and evaluation are split by whole origin game, so a held-out root's own game contributed nothing.
6. Improve conservatively. The learned ordering is used as a bounded counterfactual improvement over the existing policy rather than as a standalone player.

scaled-sibling-advantage-lab.cpp is the same experiment with more data and more model capacity, reusing the aligned-tape implementation unchanged so that scale is the only variable.

What happened

The learned ordering did not beat exact search on positions it had not seen — and the scaled repeat, with more data and a bigger model, did not close the gap either. The index records that regret against exact search went up, not down, with scale.

That second sentence is the valuable one. It is a direct test of the most common response to a failed learned evaluator ("train it on more") and the answer, at this scale and with this design, was no.

The technical record

Two rows in the experiment index, both task-record only:

• "Sibling-advantage ranker … Learns relative returns for every legal sibling under aligned continuation tapes. Rejected — task-record only; the learned ordering did not beat exact search on unseen roots."
• "Scaled sibling-advantage study … Increases data and capacity while retaining grouped, whole-origin splits. Rejected — task-record only; more data still increased regret relative to exact search."

There is no retained result beyond those two sentences: no accuracy, no regret figure, no cohort. A search of the ledger for either source returns nothing.

Both sources are locked to historical 7,000-point Sequence scoring. Each carries static_assert(kLevelBonus == 7'000), which is a deliberate barrier preventing them from being rerun against the corrected 17,000-point Hardcore engine and silently producing numbers that look comparable to current work. Any revival of this idea has to be ported into a new experiment, not unlocked; that is the repository's rule for archival sources.

What this taught us, and what is still open

This is the honest precursor of the repository's most recent line. The two ingredients that the later distributional afterstate ranker depends on — labelling every legal sibling, and aligning the chance events across siblings so the comparison is fair — were already here, and already implemented correctly.

What the later work added is a measurement this directory never made: how stable the labels themselves are. The sources here use 24 aligned continuation tapes over a 60-move horizon per sibling (read from the source, so repository-verified). When the afterstate line checked stability directly, eight aligned scenarios per sibling left the target ranking so noisy that no model could be certified against it, 64 was still not enough, and 256 was needed before the ordering held up when the scenarios were split in half (results RS-20260820T094500Z-5c1e9a04, RS-20260820T114500Z-2b7c9e31 and RS-20260820T142500Z-8f4a2d17, machine-readable records). Nothing retained here reports a comparable diagnostic at 24 tapes — so "the learned ordering was wrong" and "the labels it was fitting were too noisy to learn" cannot be told apart from what this repository kept.