9de9220214
Update RESULTS.md with: - §2: GPT-5.4-nano target yields +11.9 pt (0.560→0.679) on SearchQA — 2× the GPT-5.5 gain, demonstrating bigger benefit where headroom exists - §4: Hyperparameter sweep confirms shipped defaults are optimal Co-authored-by: Claude Opus 4 <noreply@anthropic.com>
SkillOpt-Sleep 😴 — deployment-time companion (preview)
SkillOpt-Sleep applies SkillOpt's discipline to your own daily usage. It gives a local coding agent a nightly sleep cycle that reviews your past sessions, replays your recurring tasks on your own API budget, and consolidates what it learns into validated long-term memory and skills — behind a held-out gate, staged for your review. The agent gets better the more you use it, with no weight training and zero inference-time overhead.
Preview. This is an early preview we are actively iterating on; interfaces and defaults may change. The engine lives in the top-level
skillopt_sleep/package with zero dependency on the paper'sskillopt/code (the validation gate is vendored).
How it works
One "night":
harvest Claude Code / Codex transcripts → mine recurring tasks → replay offline
→ consolidate (reflect → bounded edit → GATE on real held-out tasks)
→ stage proposal → (you) adopt
It synthesizes SkillOpt (validation-gated bounded text edits), Claude Dreams (offline consolidation; review-then-adopt), and the agent-sleep idea (short-term experience → long-term competence).
How to use it
One engine, thin per-agent shells (see plugins/):
| Platform | Folder | Install |
|---|---|---|
| Claude Code | plugins/claude-code |
/plugin marketplace add ./plugins/claude-code → /skillopt-sleep |
| Codex | plugins/codex |
bash plugins/codex/install.sh → skillopt-sleep skill |
| Copilot | plugins/copilot |
register plugins/copilot/mcp_server.py as an MCP server |
Deterministic proof (no API key):
python -m skillopt_sleep.experiments.run_experiment --persona researcher --assert-improves.
Opt-in: experience replay & dream rollouts
Two consolidation mechanisms, both default off (behavior is unchanged unless you enable them). They strengthen the nightly update when your tasks have a clean correctness signal; the validation gate still governs what ships.
| Config knob | Default | Effect |
|---|---|---|
dream_rollouts |
1 |
Run each task K times → learn from the good-vs-bad contrast (contrastive reflection). |
recall_k |
0 |
Associative recall — pull the K most-similar past tasks (from a persisted archive) into tonight's dream. |
dream_factor |
0 |
Add N lightweight synthetic variants of each task. |
Results
📊 More results & analysis — the gate-safety stress test, experience-replay scaling, and the dream-diversity ablation — are in
docs/sleep/RESULTS.md. The highlights:
Protocol (identical for every row below). 5 nights × 10 new real "today" tasks
per night; the full held-out test split is scored before night 1 (baseline) and
after night 5 (after); optimizer = GPT-5.5; single seed (42); run through the exact
shipped engine (skillopt_sleep.dream.dream_consolidate). Numbers are absolute
held-out accuracy; Δ = after − baseline in percentage points.
(a) End-to-end on real agents — gbrain-evals skillopt-v1.
Deficient seed skills go 0.00 → 1.00 on the held-out set with both Claude Code
and Codex as the target agent (all 4 seeds, including a real tool-use loop).
(b) Experience replay scales the gain — SearchQA (1,400-item held-out test, SQuAD exact-match; target = GPT-5.5; validation-gated):
Replay config (dream_rollouts=5) |
Baseline → After | Δ (pts) |
|---|---|---|
recall_k=10 |
0.802 → 0.834 | +3.1 |
recall_k=20 |
0.803 → 0.848 | +4.5 |
| full-history replay (reference, not a shipping default) | 0.796 → 0.851 | +5.6 |
recall_k=10, dream_rollouts=8 (more dreaming, same recall) |
0.798 → 0.835 | +3.7 |
The gain rises monotonically with how much relevant past experience is recalled. The
same SearchQA cell without the gate (recall_k=10) is 0.808 → 0.839 (+3.1).
(c) Second benchmark — SpreadsheetBench (280-item held-out test; the agent's generated openpyxl code is executed and compared cell-by-cell to a golden workbook; target = GPT-5.4-nano; gate-free + the output-contract guardrail): 0.279 → 0.314 (+3.6).
(d) Honest scope. These gains hold where tasks recur and have a checkable correctness signal. On saturated or noisy benchmarks (e.g. a strong model already near ceiling) the effect is flat within run-to-run noise — single-seed baseline variance here is ±1–2 pts, so treat sub-~1.5 pt differences as noise. The validation gate keeps the worst case bounded; keep it on by default.
Learn more
Full reference (pipeline, the three plugins, the experience-replay knobs) is in the Documentation & Reproduction Guide.