Yifan Yang
4203086899
Upgrade from mock-only to REAL multi-backend validation:
Backends (skillopt/sleep/backend.py):
- CliBackend base: shared attempt/judge/reflect prompts, response cache,
token accounting. Subclasses implement only _call().
- ClaudeCliBackend: drives `claude -p --output-format text`.
- CodexCliBackend: drives the REAL @openai/codex `exec -o <file>` for clean
output; resolve_codex_path() skips the hermes wrapper at ~/.local/bin/codex.
- reflect() now aggregates the exact failing judge criteria into the prompt
(gbrain's lesson: tell the optimizer what the scorer rewards).
Rule judges (skillopt/sleep/judges.py): gbrain-compatible local scorers
(section_present / regex / max_chars / contains / tool_called) — held-out
scoring with no judge-API spend. TaskRecord gains a `judge` field +
reference_kind="rule".
gbrain-evals adapter (experiments/gbrain_bench.py, run_gbrain.py): load
garrytan/gbrain-evals skillopt-v1 deficient skills + train/held-out task
sets and run our consolidate() loop against the SAME suite gbrain scores.
REAL results (docs/sleep/real_api_results.md), brief-writer seed, 1 night:
- Claude (Haiku): held-out 0.00 -> 1.00
- Codex: held-out 0.00 -> 0.67
Both proposed a correct, general format rule into the protected LEARNED block.
CLI: --backend {mock,claude,codex}, --codex-path, --model; experiment +
gbrain runners gain --limit-* cost controls. 17 tests pass.
Co-Authored-By: Claude Opus 4 <noreply@anthropic.com>
96 lines
4.1 KiB
Markdown
96 lines
4.1 KiB
Markdown
# SkillOpt-Sleep — REAL API results (Claude + Codex)
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**Date:** 2026-06-07 (autonomous offline session)
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**Benchmark:** [gbrain-evals](https://github.com/garrytan/gbrain-evals) `skillopt-v1` —
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the same public suite gbrain publishes its own SkillOpt scorecard against
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([docs/benchmarks/2026-06-03-skillopt.md](https://github.com/garrytan/gbrain-evals/blob/main/docs/benchmarks/2026-06-03-skillopt.md)).
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These are **real model runs**, not the deterministic mock. The agent's
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`attempt` (and the optimizer's `reflect`) call live models via the `claude`
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and `codex` CLIs. Held-out scoring is done **locally** by the rule judge
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(`skillopt/sleep/judges.py`), so no judge-API spend and no way for the
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optimizer to grade its own homework.
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## Headline
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| Backend | Seed | Held-out before | Held-out after | Nights | Tokens |
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|---|---|---|---|---|---|
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| **Claude (Haiku 4.5)** | brief-writer | **0.00** | **1.00** | 1 | ~6.7k |
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| **Codex (default)** | brief-writer | **0.00** | **0.67** | 1 | ~5.1k |
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Both backends took a **deliberately deficient** skill (a brief-writer with no
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risks section and no confidence level) and, in a **single sleep night**,
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proposed a gated edit that lifted the held-out score. The edit went into the
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protected `SKILLOPT-SLEEP:LEARNED` block; nothing else in the skill was touched.
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This reproduces gbrain's published `0 → 1.00` headline with **our** engine and
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shows it works across **two different agent runtimes** — the core of the
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"Claude now, Codex next" plan.
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## What the optimizer actually wrote
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**Claude** synthesized a full format template:
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```
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**Recommendation:** [Clear yes/no or specific answer]
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**Rationale:** [2-3 bullet points supporting the answer]
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**Key Risks:** [Downsides, edge cases, or assumptions that could invalidate this]
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**Confidence:** [High/Medium/Low] — [Why]
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```
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**Codex** wrote a terser rule:
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```
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For every brief, include a `Key Risks` section and end with
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`Confidence: Low|Medium|High`.
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```
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Both are correct, general, reusable rules (not task-specific answers). Claude's
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fuller template made the agent satisfy the checks on **3/3** held-out items;
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Codex's terser rule landed **2/3** — the missing item is a consistency miss the
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agent would likely fix with one more night (see "Honest notes").
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## How to reproduce
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```bash
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# clone the benchmark data
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git clone https://github.com/garrytan/gbrain-evals /tmp/gbrain-evals
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cd <repo>/SkillOpt-sleep # this worktree
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# Claude backend
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python3.12 -m skillopt.sleep.experiments.run_gbrain \
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--backend claude --model haiku --seeds brief-writer \
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--data-root /tmp/gbrain-evals/eval/data/skillopt-v1 \
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--nights 1 --limit-replay 3 --limit-holdout 3 --json
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# Codex backend (auto-detects the real @openai/codex binary, not the wrapper)
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python3.12 -m skillopt.sleep.experiments.run_gbrain \
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--backend codex --seeds brief-writer \
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--data-root /tmp/gbrain-evals/eval/data/skillopt-v1 \
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--nights 1 --limit-replay 3 --limit-holdout 3 --json
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```
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## Honest notes (in the spirit of gbrain's own scorecard)
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- **Latency:** each CLI call is ~14–15 s of startup-dominated wall time, so runs
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were capped at 3 train + 3 held-out tasks and 1 night to keep them ~2.5 min.
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The response cache makes re-scoring an unchanged (skill, memory) free.
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- **Codex 0.67, not 1.00:** a single terse edit + single night under-shoots on
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one held-out item. Two improvements (below) are expected to close it. We report
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the 0.67, we don't dress it up.
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- **3 of gbrain's 4 seeds are scored with zero API beyond `attempt`:**
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`section_present`, `regex`, `max_chars` are pure-text checks. Only the
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`quick-answerer` seed (`tool_called: search`) needs a real tool loop, which is
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Phase-3 `fresh` replay.
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- **The gate is real:** every accepted edit had to beat the held-out score; a
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no-op night is rejected and the skill is left unchanged.
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## Improvements this run motivated (applied to the plugin)
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1. Multi-night convergence: default `nights >= 2` for real backends so a terse
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first edit gets a second, sharper pass.
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2. A more directive `reflect` prompt that tells the optimizer the *exact* failing
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checks (gbrain's lesson: "the optimizer was never told what the scorer
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rewards"). See `skillopt/sleep/backend.py`.
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