docs(sleep): full sweep — 5/5 direct + 4/4 transfer all 0->1.00

Machine-generated benchmark_report.md from a 9-config sweep:
  - Direct (Sonnet->Haiku): brief-writer/advisor/thorough-analyst 0->1.00
  - Direct (Codex): brief-writer/advisor 0->1.00
  - Transfer (4/4 positive, incl. cross-runtime Codex<->Claude): all 0->1.00

Cross-model transfer confirms the price-difference value prop: a skill
optimized on a cheap model deploys for free on an expensive one, and skills
move between Codex and Claude. sweep.jsonl is the committed source data.

Co-Authored-By: Claude Opus 4 <noreply@anthropic.com>
This commit is contained in:
Yifan Yang
2026-06-08 14:31:51 +00:00
parent 4186e5bb73
commit b1f41a7506
3 changed files with 65 additions and 5 deletions
+17 -5
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@@ -93,12 +93,24 @@ weak and what changed.
> *Optimize cheap overnight, deploy anywhere.* A skill is just text, so a good
> rewrite should help a model it was never optimized on.
The sweep runs these pairs (optimize on SOURCE, freeze, evaluate held-out on
TARGET with no further optimization). See `benchmark_report.md` / `sweep.jsonl`
for the auto-generated table once the sweep completes:
Optimize on SOURCE, **freeze** the learned skill, evaluate held-out on TARGET with
no further optimization. All four pairs are positive — including **across
runtimes** (Codex ↔ Claude):
- Haiku → Sonnet, Sonnet → Haiku (within Claude)
- Codex → Claude, Claude → Codex (across runtimes)
| Source (optimizer) | Target (deploy) | Seed | Target baseline → transferred | Gain |
|---|---|---|---|---|
| Claude Haiku (cheap) | Claude Sonnet (expensive) | brief-writer | 0.00 → **1.00** | +1.00 |
| Claude Sonnet | Claude Haiku | brief-writer | 0.00 → **1.00** | +1.00 |
| **Codex** | **Claude Haiku** | brief-writer | 0.00 → **1.00** | +1.00 |
| **Claude Haiku** | **Codex** | brief-writer | 0.00 → **1.00** | +1.00 |
**4/4 transfers positive.** A skill optimized on a cheap model deploys for free on
an expensive one, and skills move between Codex and Claude — the Sleep-setting
analogue of SkillOpt's cross-model and cross-harness transfer tables. This is the
quantified answer to "optimize cheap overnight, deploy anywhere."
Full machine-generated scorecard: [`benchmark_report.md`](benchmark_report.md)
(source data `sweep.jsonl`).
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