Experiment · sql

SQL candidate selection

Candidate selection remains a plausible future method, but an unfrozen recipe is not unfinished evidence for this project. Retained outcome: rejected.

Outcome
rejected
Family
sql
Evidence confidence
exact
Record
training run

Evidence readout

tooling and smoke exist, but no frozen candidate-selection dataset, baseline, or model run was approved before project closure

What the attempt taught

Candidate selection remains a plausible future method, but an unfrozen recipe is not unfinished evidence for this project.

Why it stopped or stayed bounded

The proposed run never acquired a frozen target/eval contract, and running it retrospectively would start a new experiment rather than complete an existing measured claim.

Method and scope

  • Experiment family: sql.
  • Record kind: training run.
  • Objective: sql execution.
  • Methods: candidate selection.
  • Base models: qwen3-0.6b.

Disposition

Closed for this project. Reopen only as a fresh owner-led experiment after the learning phase with new data and a frozen gate.

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.