Yifan Yang
de3be75bac
Adds docs/sleep/README.md — a concise intro to the SkillOpt-Sleep plugin (what it is, how to use it across the three agents, the opt-in experience-replay / dream-rollout knobs, and headline results), linking to the full guide section. Adds a News bullet pointing to it. No code changes.
78 lines
3.7 KiB
Markdown
78 lines
3.7 KiB
Markdown
# SkillOpt-Sleep 😴 — deployment-time companion (preview)
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**SkillOpt-Sleep** applies SkillOpt's discipline to your *own daily usage*. It gives a
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local coding agent a nightly **sleep cycle** that reviews your past sessions, replays
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your recurring tasks on your own API budget, and consolidates what it learns into
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**validated** long-term memory and skills — behind a held-out gate, staged for your
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review. The agent gets better the more you use it, with **no weight training** and
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**zero inference-time overhead**.
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> **Preview.** This is an early preview we are actively iterating on; interfaces and
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> defaults may change. The engine lives in the top-level [`skillopt_sleep/`](../../skillopt_sleep)
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> package with **zero dependency** on the paper's `skillopt/` code (the validation gate
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> is vendored).
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## How it works
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One "night":
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```
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harvest Claude Code / Codex transcripts → mine recurring tasks → replay offline
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→ consolidate (reflect → bounded edit → GATE on real held-out tasks)
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→ stage proposal → (you) adopt
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```
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It synthesizes **SkillOpt** (validation-gated bounded text edits), **Claude Dreams**
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(offline consolidation; review-then-adopt), and the **agent-sleep** idea (short-term
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experience → long-term competence).
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## How to use it
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One engine, thin per-agent shells (see [`plugins/`](../../plugins)):
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| Platform | Folder | Install |
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| **Claude Code** | [`plugins/claude-code`](../../plugins/claude-code) | `/plugin marketplace add ./plugins/claude-code` → `/skillopt-sleep` |
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| **Codex** | [`plugins/codex`](../../plugins/codex) | `bash plugins/codex/install.sh` → `skillopt-sleep` skill |
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| **Copilot** | [`plugins/copilot`](../../plugins/copilot) | register `plugins/copilot/mcp_server.py` as an MCP server |
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Deterministic proof (no API key):
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`python -m skillopt_sleep.experiments.run_experiment --persona researcher --assert-improves`.
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### Opt-in: experience replay & dream rollouts
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Two consolidation mechanisms, both default **off** (behavior is unchanged unless you
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enable them). They strengthen the nightly update when your tasks have a clean
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correctness signal; the validation gate still governs what ships.
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| Config knob | Default | Effect |
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| `dream_rollouts` | `1` | Run each task K times → learn from the good-vs-bad contrast (contrastive reflection). |
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| `recall_k` | `0` | Associative recall — pull the K most-similar past tasks (from a persisted archive) into tonight's dream. |
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| `dream_factor` | `0` | Add N lightweight synthetic variants of each task. |
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## Results
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- **End-to-end on real agents.** On the public
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[gbrain-evals](https://github.com/garrytan/gbrain-evals) `skillopt-v1` benchmark,
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deficient seed skills go **0.00 → 1.00** on held-out sets with **both Claude and
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Codex** (all 4 seeds, including a real tool-use loop).
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- **Experience replay scales the gain on a clean signal** (deployment protocol:
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5 nights × 10 new real tasks/night, full held-out test, GPT-5.5, gated):
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| Config | Δ vs baseline |
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| `recall_k=10, dream_rollouts=5` | +3.1 pts |
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| `recall_k=20, dream_rollouts=5` | **+4.5 pts** |
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| full-history replay (reference) | +5.6 pts |
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A second benchmark (SpreadsheetBench, GPT-5.4-nano, gate-free) gives **+3.6 pts**.
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- **Honest scope.** Gains are real where tasks recur and have a checkable correctness
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signal; on saturated or noisy tasks the effect is flat within run-to-run noise
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(±1–2 pts, single seed). The validation gate keeps the downside bounded — keep it on.
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## Learn more
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Full reference (pipeline, the three plugins, the experience-replay knobs) is in the
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**[Documentation & Reproduction Guide](https://microsoft.github.io/SkillOpt/docs/guideline.html#sleep)**.
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