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.
105 lines
5.7 KiB
Markdown
105 lines
5.7 KiB
Markdown
# SkillOpt: Executive Strategy for Self-Evolving Agent Skills
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*Train agent skills like you train neural networks — with epochs, (mini-)batchsize, learning rates, and validation gates — but without touching model weights.*
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[](https://microsoft.github.io/SkillOpt/) [](https://arxiv.org/abs/2605.23904) [](https://youtu.be/JUBMDTCiM0M) [](https://pypi.org/project/skillopt/) [](https://www.python.org/) [](LICENSE)
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> 📖 **For installation, data preparation, training/eval commands, the full configuration reference, and framework internals, see the [Documentation & Reproduction Guide](https://microsoft.github.io/SkillOpt/docs/guideline.html)** (rendered on GitHub Pages).
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---
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## News 🔥🔥🔥
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- **[2026-06-15]** 😴 **SkillOpt-Sleep (preview)** — a nightly offline self-evolution companion for local coding agents (Claude Code / Codex / Copilot): review past sessions, replay recurring tasks, and consolidate validated skills behind a held-out gate. See **[`docs/sleep/README.md`](docs/sleep/README.md)** for what it is, how to use it, and results.
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- **[2026-06-03]** 🎉 **[gbrain](https://github.com/garrytan/gbrain), [gbrain-evals](https://github.com/garrytan/gbrain-evals/blob/main/docs/benchmarks/2026-06-03-skillopt.md), and [darwin-skill](https://github.com/alchaincyf/darwin-skill) have all integrated SkillOpt.**
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- **[2026-06-02]** 🎉 **SkillOpt [v0.1.0](https://github.com/microsoft/SkillOpt/releases/tag/v0.1.0) is now available on [PyPI](https://pypi.org/project/skillopt/)!** Install with `pip install skillopt`. This initial release includes the full training loop (rollout → reflect → aggregate → select → update → evaluate), multi-backend support (OpenAI / Azure / Claude / Qwen / MiniMax), six built-in benchmarks, and WebUI dashboard.
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---
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## Overview
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Modern agent skills are usually hand-crafted, generated one-shot by a strong
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LLM, or evolved through loosely controlled self-revision — none of which
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behaves like a deep-learning optimizer for the skill itself, and none of
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which reliably improves over its starting point under feedback.
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**SkillOpt treats the skill document as the trainable state of a frozen
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agent**, and trains it with the discipline that makes weight-space
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optimization reproducible. A separate optimizer model turns scored rollouts
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into bounded add / delete / replace edits on a single skill document; a
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candidate edit is accepted only when it strictly improves a held-out
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validation score. A textual learning-rate budget, a rejected-edit buffer,
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and an epoch-wise slow / meta update make skill training stable while
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adding **zero inference-time model calls** at deployment.
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The deployed artifact is a compact `best_skill.md` (typically 300–2,000
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tokens) that runs against the unchanged target model. Across **six
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benchmarks, seven target models, and three execution harnesses** (direct
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chat, Codex CLI, Claude Code CLI), SkillOpt is best or tied-best on **all
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52 evaluated (model, benchmark, harness) cells** and on GPT-5.5 lifts the
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average no-skill accuracy by **+23.5 points in direct chat, +24.8 inside
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the Codex agentic loop, and +19.1 inside Claude Code**. Optimized skill
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artifacts transfer across model scales, between Codex and Claude Code
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harnesses, and to nearby benchmarks without further optimization.
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For the full method, ablations, and per-cell results see the [paper](https://arxiv.org/abs/2605.23904); for a visual walkthrough of the loop see the [project page](https://microsoft.github.io/SkillOpt/); for deeper API / backend / benchmark docs see [`docs/`](docs/).
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## 🎬 Demo Video
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https://github.com/user-attachments/assets/eb12d3bc-371c-467f-904d-91b61f339ed7
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<p align="center">
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<a href="https://youtu.be/JUBMDTCiM0M"><b>▶ Watch the full demo on YouTube</b></a>
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</p>
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---
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## Extensibility & WebUI
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### Adding a new backend
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A backend = a chat / exec target (e.g. `openai_chat`, `claude_chat`,
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`qwen_chat`, `minimax_chat`, `codex_exec`, `claude_code_exec`). See
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[`docs/guide/new-backend.md`](docs/guide/new-backend.md) for the full
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contract; in short you add a `skillopt/model/<name>_backend.py` module,
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register it in `skillopt/model/common.py` + `backend_config.py`, and wire
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it through the router in `skillopt/model/__init__.py`. `qwen_backend.py`
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and `minimax_backend.py` are good templates.
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### Adding a new benchmark
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A benchmark = a `skillopt/envs/<name>/` package with a `dataloader.py`, a
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`rollout.py`, and an `initial.md` seed skill. See
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[`docs/guide/new-benchmark.md`](docs/guide/new-benchmark.md) for the full
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contract; the simplest reference is `skillopt/envs/searchqa/`.
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### WebUI
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Launch the monitoring dashboard (optional):
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```bash
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pip install -e ".[webui]"
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python -m skillopt_webui.app
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```
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| Flag | Default | Description |
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|---|---|---|
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| `--port` | 7860 | Server port |
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| `--host` | `0.0.0.0` | Bind address |
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| `--share` | off | Create a public Gradio share link |
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---
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## Citation
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```bibtex
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@misc{yang2026skilloptexecutivestrategyselfevolving,
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title={SkillOpt: Executive Strategy for Self-Evolving Agent Skills},
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author={Yifan Yang and Ziyang Gong and Weiquan Huang and Qihao Yang and Ziwei Zhou and Zisu Huang and Yan Li and Xuemei Gao and Qi Dai and Bei Liu and Kai Qiu and Yuqing Yang and Dongdong Chen and Xue Yang and Chong Luo},
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year={2026},
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eprint={2605.23904},
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archivePrefix={arXiv},
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primaryClass={cs.AI},
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url={https://arxiv.org/abs/2605.23904}
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}
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```
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