Experiment · pace planner

Pace clarify-v1 Qwen3-4B LoRA

Fine-tuning a strong base on a thin behavior slice requires coverage for every dimension that must not regress. Retained outcome: regressed.

Outcome
regressed
Family
pace planner
Evidence confidence
inferred
Record
training run

Evidence readout

38 contrastive rows moved ambig only `0% -> 5%` while OOS regressed `80% -> 33%` in the retrospective; drilldown records the same attempt as `2%/40%/50%` on ambig/oos/destructive

What the attempt taught

Fine-tuning a strong base on a thin behavior slice requires coverage for every dimension that must not regress.

Why it stopped or stayed bounded

Small-corpus LoRA on a competent base caused catastrophic interference across planner dimensions.

Method and scope

  • Experiment family: pace planner.
  • Record kind: training run.
  • Objective: ambiguity detection.
  • Methods: lora.
  • Base models: qwen3-4b.
  • Recorded data rows: 38.

Disposition

Do not fine-tune the planner on narrow clarify rows unless the corpus also covers OOS, destructive, happy path, and breadth gates.

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.