Search-guided self-play with every-sibling labels converts compute into long-horizon strength

Every learned board-scorer in this repository has been trained once, from data generated by something else, and then judged. This theory asks a different question: what if the data and the scorer improved each other in a loop?

The proposal is the AlphaZero pattern, adapted to a one-player game with luck and a hard information boundary. Many games run at once. At every position, a fair search evaluates every legal column under shared imagined futures — successor-closed data by construction, which is exactly what the sibling trap kept punishing. A learner trains a board evaluator on those labels. The trained evaluator then becomes the leaf of the next round's search, which produces better labels than the evaluator that guided it, and the cycle repeats. That feedback is the reason more compute might buy longer-horizon play rather than a more confident copy of the same bias.

The candidate is the frozen public student that falls out of any iteration; the comparator is unchanged fair D4. The full build, the staging order, and where each stage would run are written up on what a large-scale run would look like.

Where it stands. Nothing has been run. The record is draft / untested, its evidence tier is proposal, and there is no implementation and no result. It exists so that the plan and its failure tests are on paper before any compute is spent — see also is more computation the answer?, which explains how often that assumption has failed here.

How you could help. The theory names its own cheap kill-shots, and all of them run on one workstation. Stage 0 is a paper audit: can existing every-sibling data seed the first dataset without opening new seeds? Stage 3 asks whether held-out sibling ranking improves across iterations on the locked panel — if three iterations do not improve, the loop is falsified before a cluster is ever requested. Stage 4 doubles actor compute with everything else held fixed and looks for a paired 32-game gain beyond the noise band. A clean negative at any of those stages is a finished contribution.