Evidence readout
validation exact `6.16% random -> 10.54%` (+4.38 points), test `6.23% -> 10.33%` (+4.10); raw and guarded policies each won 0/6 games against random legal play
What the attempt taught
Legal constrained decoding and lower move-prediction loss are not game intelligence; require full-game transfer before scaling a tiny specialist.
Why it stopped or stayed bounded
Completion-only SFT learned a small held-out move preference but missed the frozen +10-point promotion gate and produced no full-game win advantage over random legal play; eight guard interventions converted no wins.
Method and scope
- Experiment family: chess.
- Record kind: training run.
- Objective: chess move selection.
- Methods: sft.
- Base models: byte-character-chess-44m.
- Recorded data rows: 10000.
Disposition
Do not run the 100k, 1M, or 2M stages under this recipe. Preserve the failed artifact and select a different bounded specialist target.
This retained disposition is historical evidence, not authorization to restart the experiment. A new run needs a fresh question, frozen evaluator, explicit resource budget, and scoped tracking issue.
How to read this dossier
This canonical page separates the retained repository decision from the material that informed it. A source may describe an external claim, historical measurement, local observation, or planned procedure; those evidence classes are not interchangeable. Follow the provenance links before reusing a number or method.
Status and disposition describe what PostTrainLLM retained when this record was indexed. They are not a live product promise, a newly run benchmark, or permission to restart historical work. Unknown or unmeasured fields remain unknown rather than being treated as zero.
To reuse the record, first name the exact claim you need and trace it to the linked source. Then check whether the original environment, model revision, data split, evaluator, hardware, and budget match the proposed use. If they do not, treat the record as a hypothesis or design reference and run the smallest fresh comparison that can falsify it. Preserve negative outcomes and regressions beside any improvement; a local win on one slice does not silently become a general capability claim.
Source provenance
The normalized record comes from docs/attempts.json. The links below are the tracked evidence and explanatory sources preserved with the record.