Successor-closed distributional afterstate values rank siblings as well as fair D4
A single action-free public-afterstate evaluator, trained only on successor-closed labels (every legal sibling at each root, aligned chance scenarios, a fixed public continuation policy), ranks the legal root actions of disjoint whole-origin games at least as accurately as fair D4, and therefore can serve as the value core of a direct 1-ply chance-averaged policy with exact D4 fallback.
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The strongest dependable player in this repository is a search that looks four of the player's own moves ahead and averages over the luck in between — fair D4. It is slow. The obvious shortcut is to train a network that looks at a board once and says how promising it is, then use it to pick a column directly. Every previous attempt here broke in the same way, described on the sibling trap page: the model learned the outcome of the move that was actually played, and then had to choose among six other columns it had never been shown.
This theory says the break is fixable by changing the data rather than the network. Make the scorer action-free — it reads only the board after a move resolves, plus the visible next disc and the rise clock, so there is no "which move was this" input to shortcut on. Then make the training data successor-closed: at every position, label every legal column, under the same imagined runs of luck, so no candidate is ever out-of-sample. If that works, the model can replace the four-move search as the thing that picks the column, with the search kept only as a fallback.
Where it stands. This theory has code, four registered experiments, and
four recorded results — it is not a paper idea. The record's own assessment is
not-supported-as-tested: once the labels were made stable enough to judge
anything, the learned scorer trailed the four-move search on every preregistered
ranking comparison. The mechanism was not shown to be broken; the specific
configuration was shown not to beat the reference.
How you could help. The results name two suspects, not one. The scorer was stopped undertrained (11 of a planned 20 epochs), and its labels came from a deliberately weak one-move continuation player. Evidence that would move this theory is a rerun of the same frozen ranking gate with a fully trained model, or with labels generated by a stronger continuation policy — either result, better or worse, is informative. Evidence that the gap survives both would close the direct-replacement reading for good.
Technical recordThe registered claim, mechanism and falsification criteria
- Claim
- A single action-free public-afterstate evaluator, trained only on successor-closed labels (every legal sibling at each root, aligned chance scenarios, a fixed public continuation policy), ranks the legal root actions of disjoint whole-origin games at least as accurately as fair D4, and therefore can serve as the value core of a direct 1-ply chance-averaged policy with exact D4 fallback.
- Mechanism
- Prior learned policies in this repository failed for a diagnosed reason: they were trained on the action the behavior policy played (or a sparse sibling sample), so deployment required ranking actions whose successors were never labeled; low value error on visited states coexisted with poor sibling ranking. Making the evaluator action-free (its input is the fully resolved public afterstate: board, next visible disc, moves-until-rise) and the corpus successor-closed (every legal sibling's afterstates receive labels under common event-keyed chance scenarios) removes both the action-identity shortcut and the coverage gap by construction: every candidate action at deployment is scored by the same function on in-support states. Distributional (quantile) targets plus a within-root ranking loss train the relative ordering the policy actually needs rather than only absolute calibration, and the multi-move continuation labels supply the long-horizon signal that a one-ply heuristic lacks.
- Falsification criteria
- On preregistered held-out whole origins, the model's scenario-mean action ranking underperforms exact fair D4's ranking of the same roots in top-1 accuracy against the H40 long-outcome target by more than the frozen margin.
- Within-root pairwise accuracy or normalized regret fails the frozen thresholds on any held-out origin fold.
- Action stability between independent scenario halves is below the frozen threshold, showing the labels themselves are too noisy to learn the ranking.
- The corpus cannot be built successor-closed (any legal sibling missing a label) at the pilot scale within the frozen compute budget.
- Information class
- public-policy
- Lifecycle
- assessed
- Assessment
- not-supported-as-tested
- Evidence tier
- pilot
- Dependencies
- none recorded
- Evidence references
research/results/RS-20260820T094500Z-5c1e9a04.jsonresearch/results/RS-20260820T114500Z-2b7c9e31.jsonresearch/results/RS-20260820T142500Z-8f4a2d17.jsonresearch/results/RS-20260820T184500Z-63c0a8e2.jsonresearch/results/RS-20260821T094500Z-1a7e3c55.jsonresearch/results/RS-20260821T134500Z-4b9d2f68.json
Agent contextHow to extend this record
To add a reader-facing explanation, write web/content/research/TH-20260820-distributional-afterstate-ranker-7aba7fb3.mdx; it renders above this record on the next request. The registered record itself is in the technical record above.
Record file: research/theories/TH-20260820-distributional-afterstate-ranker-7aba7fb3.json, validated against research/schemas/theory-v1.schema.json.
Registered by OpenCode / moonshotai/Kimi-K3.