---
title: Learned-guidance search
family: tree-search
summary: Let a small learned board evaluator decide where a deeper search should spend its time, while keeping the exact search as a safety net.
status: rejected
evidence: ledger-recorded
reads: public
---

Let a small learned board evaluator decide where a deeper search should spend
its time, while keeping the exact search as a safety net.

<EvidenceLabel status="rejected" evidence="ledger-recorded" reads="public" />

## The intuition

A complete look-ahead spends the same effort on every column, including the
ones a competent player would dismiss instantly. If something could tell the
search which branches are worth expanding, the same budget would reach further
ahead. That "something" here is a small learned evaluator of the public
position: an [NNUE](/learn/glossary)-style network trained to reproduce a
stronger search's judgements.

The design is deliberately timid, and that is its most interesting feature.
The learned model is never allowed to be the last word:

- **Exact first.** Complete the full-width sampled-chance depth-3 search to a
  finished answer. Only then spend whatever budget remains on learned-guided
  deeper iterations, and only accept a deeper answer if that whole iteration
  finished.
- **Root actions are untouchable.** The model may order and prune *interior*
  branches; every legal column at the current position is always searched.

Three programs sit in this directory. Two of them test guidance; the third
tests the opposite lever — spending the extra budget on covering more chance
outcomes at the root instead of on more depth — using the same fixed model and
feature code so the comparison is clean.

## How it works, step by step

1. Read the public position and run the exact depth-3 search to completion.
   Record its answer.
2. With the remaining fixed work budget, run deeper iterations in which the
   learned evaluator orders and reduces interior choice nodes.
3. If a deeper iteration completes within budget, and only then, allow its
   answer to replace the depth-3 answer.
4. Play that column.

The root-quadrature variant replaces step 2 with a wider treatment of chance:
three first-reveal strata crossed with all seven possible next discs, followed
by three joint interior strata.

## What happened

Every version was worse than the plain depth-3 search it was trying to
improve, and the ones that spent the most extra work were the worst.

The safeguarded deeper search spent nearly its whole extra budget, actually
completed a deeper iteration on about a fifth of moves, and changed the depth-3
answer on fewer than one move in twenty, and still came out behind on score
and on how long the games lasted. That combination is the finding: a rare
learned override is not harmless, because the rare occasions on which a model
disagrees with an exact search are exactly the occasions where its errors are
largest. The wider chance coverage at the root failed the same way for a
different reason. It spent about 239,000 work units per move against about
58,000 for plain depth 3 and still lost to it by a wide margin, showing that
better sampling of chance does not repair an evaluator that is biased at the
horizon.

The third program, which re-searched only the actions the model liked, is
recorded as rejected but has no entry in the written ledger at all. Its number
exists only in a research conversation, so there is nothing here to quote.

Two limitations must travel with all of this. These screens are four games
each, which is small even for a screen. And they were decided under the
historical 7,000-point Sequence scoring, which the repository treats as
archival: the level bonus is now 17,000 points and carries the overwhelming
majority of a Hardcore score, so a result that traded score against survival
could plausibly change sign under the corrected rules. No corrected re-run of
any of these three is on record.

<TechnicalDetails title="The technical record">

**Learned deeper-search override** — rejected, ledger-recorded. On four
historical 7,000-point Sequence-scored games, the safeguarded policy averaged
82,169.25 points and 58.75 moves versus 87,743.75 and 62 for depth 3. It spent
235,654 work units per move, completed a deeper iteration on 22.1% of moves,
and changed the depth-3 action on 4.68% of moves. Because even the screen mean
was worse, the reserved confirmation seeds were not read.

**Root reveal quadrature** — rejected, ledger-recorded. Its predeclared
absolute gate was the full-width sampled-chance depth-3 policy. On four
historical 7,000-point Sequence-scored games, depth 3 averaged 205,001.25
points and 132.5 moves while the larger quadrature averaged 179,243 and 120. It
used about 239,000 work units per move against about 58,000, failed the screen,
and confirmation was not run.

**Selective learned search** — rejected, task-record only. There is no ledger
protocol, no artifact hash and no retained per-game data;
[audit 04](/docs/exploratory/audit-04-blind-spots) lists this source among the
thirty rejections that "cannot be re-derived". From the source it re-searches
only model-ranked promising actions while retaining the frozen exact fallback.

**A caveat on "fresh" seeds.** The ledger describes the seed ranges for the
deeper-search and quadrature screens as fresh.
[Audit 03](/docs/exploratory/audit-03-claim-arithmetic): an exploratory,
read-only audit, not a re-measurement — finds those ranges fall inside the
first 100,000-game n-tuple training block, at offsets 45,056 and 49,152, and
places the selective-search screen and confirmation ranges at offset 36,864 of
the same block. That does not change these rejections, which are against a
comparator on the same games, but it means the games were not new to the
repository.

**Scoring.** Unlike their observable-MCTS neighbours, these three sources carry
no compile-time 7,000-point lock, so they build against today's corrected
17,000-point engine. Re-running them therefore would *not* reproduce the
numbers above.

Sources: "Learned deeper-search override (rejected)" and "Root reveal
quadrature (rejected)" in the [experiment history](/docs/research/history); the
tree-search table in the [experiment index](/docs/research/experiment-index).

</TechnicalDetails>

## What this taught us, and what is still open

- **A rare override is a risky override.** Changing under 5% of decisions still
  lost the game, because a model disagrees with an exact search precisely where
  it is least reliable. The same shape appears again in the
  [terminal policy iteration](/approaches/terminal-policy-iteration) family.
- **More chance coverage is not a substitute for a better leaf.** The
  quadrature arm isolated that lever and it did not help.
- **The current guidance is explicit about ordering.** The research roadmap
  states: "Do not begin with learned action pruning. Every legal root action
  remains eligible until the model has independently demonstrated safe sibling
  ranking." These results are why.
- **What was ruled out** is these configurations under old scoring on
  four-game screens; what was not ruled out is learned guidance in general,
  which remains untested under corrected Hardcore scoring on a cohort large
  enough to decide anything.