mirror of
https://github.com/microsoft/SkillOpt.git
synced 2026-07-07 00:15:39 +08:00
4a1b984d87
- Rename teacher -> optimizer, student -> target across all code, configs, docs, prompts - CLI: --teacher_model -> --optimizer_model, --student_model -> --target_model - Remove best_skill files, keep only initial skills - Fix slow update gate (force write into skill) - Fix SLOW_UPDATE marker stripping - Remove deep_reflect and meta_reflect mechanisms - Update .env.example with export prefix and azure_cli docs - Add endpoint empty validation in azure_openai.py Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
171 lines
4.5 KiB
Markdown
171 lines
4.5 KiB
Markdown
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hide:
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- navigation
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---
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<div class="hero" markdown>
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# SkillOpt
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### Train Agent Skills Like Neural Networks
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*Optimize natural-language skill documents through iterative rollout, reflection, and gated validation — with epochs, learning rates, and validation gates — without touching model weights.*
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[Get Started :material-rocket-launch:](guide/installation.md){ .md-button .md-button--primary }
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[View on GitHub :material-github:](https://github.com/microsoft/SkillOpt){ .md-button }
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</div>
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---
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## How It Works
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<div class="pipeline-container" markdown>
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<div class="pipeline-wrapper">
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<div class="pipeline-stage" id="stage-rollout">
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<div class="stage-icon">🎯</div>
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<div class="stage-label">Rollout</div>
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<div class="stage-desc">Target executes tasks</div>
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</div>
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<div class="pipeline-arrow"><div class="flow-line"></div></div>
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<div class="pipeline-stage" id="stage-reflect">
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<div class="stage-icon">🔍</div>
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<div class="stage-label">Reflect</div>
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<div class="stage-desc">Optimizer analyzes trajectories</div>
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</div>
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<div class="pipeline-arrow"><div class="flow-line"></div></div>
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<div class="pipeline-stage" id="stage-aggregate">
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<div class="stage-icon">🔗</div>
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<div class="stage-label">Aggregate</div>
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<div class="stage-desc">Merge edit patches</div>
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</div>
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<div class="pipeline-arrow"><div class="flow-line"></div></div>
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<div class="pipeline-stage" id="stage-select">
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<div class="stage-icon">✂️</div>
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<div class="stage-label">Select</div>
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<div class="stage-desc">Rank & clip edits</div>
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</div>
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<div class="pipeline-arrow"><div class="flow-line"></div></div>
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<div class="pipeline-stage" id="stage-update">
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<div class="stage-icon">📝</div>
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<div class="stage-label">Update</div>
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<div class="stage-desc">Apply to skill doc</div>
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</div>
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<div class="pipeline-arrow"><div class="flow-line"></div></div>
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<div class="pipeline-stage" id="stage-gate">
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<div class="stage-icon">🚦</div>
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<div class="stage-label">Gate</div>
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<div class="stage-desc">Validate & accept</div>
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</div>
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</div>
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<div class="pipeline-epoch-bar">
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<div class="epoch-mechanism">🔄 Slow Update</div>
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<div class="epoch-mechanism">🧠 Meta Skill</div>
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<div class="epoch-label">Epoch Boundary</div>
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</div>
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</div>
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---
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## Deep Learning Analogy
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SkillOpt brings the familiar deep-learning training paradigm to agentic prompt optimization:
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| Deep Learning | SkillOpt |
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|---|---|
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| Model weights | Skill document (Markdown) |
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| Forward pass | Rollout (target executes tasks) |
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| Loss / gradient | Reflect (optimizer produces edit patches) |
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| Gradient clipping | Edit selection (`learning_rate` = max edits) |
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| SGD step | Patch application to skill |
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| Validation set | Gated evaluation on selection split |
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| LR schedule | `lr_scheduler`: cosine, linear, constant |
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| Epochs | Multi-epoch with slow update & meta skill memory |
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---
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## Supported Benchmarks
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| Benchmark | Type | Config |
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|---|---|---|
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| **DocVQA** | Document QA | `configs/docvqa/` |
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| **ALFWorld** | Embodied AI | `configs/alfworld/` |
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| **OfficeQA** | Enterprise QA | `configs/officeqa/` |
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| **SearchQA** | Open-domain QA | `configs/searchqa/` |
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| **LiveMathBench** | Math reasoning | `configs/livemathematicianbench/` |
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| **SWEBench** | Software Engineering | `configs/swebench/` |
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| + 5 more | Various | See [docs](guide/first-experiment.md) |
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---
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## Quick Example
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```bash
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# Install
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pip install -e .
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# Configure credentials
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export AZURE_OPENAI_ENDPOINT="https://your-resource.openai.azure.com/"
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export AZURE_OPENAI_API_KEY="your-key"
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# Train on SearchQA
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python scripts/train.py --config configs/searchqa/default.yaml
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# Evaluate best skill
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python scripts/eval_only.py \
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--config configs/searchqa/default.yaml \
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--skill outputs/best_skill.md
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```
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---
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<div class="grid cards" markdown>
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- :material-book-open-variant:{ .lg .middle } **Getting Started**
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---
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Install SkillOpt, configure your API keys, and run your first experiment in 5 minutes.
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[:octicons-arrow-right-24: Installation](guide/installation.md)
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- :material-puzzle:{ .lg .middle } **Add a Benchmark**
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---
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Extend SkillOpt with your own benchmark in ~100 lines of code.
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[:octicons-arrow-right-24: Extension Guide](guide/new-benchmark.md)
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- :material-cog:{ .lg .middle } **Configuration**
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---
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Full reference for all hyperparameters with deep learning analogies.
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[:octicons-arrow-right-24: Config Reference](reference/config.md)
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- :material-monitor-dashboard:{ .lg .middle } **WebUI**
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---
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Configure, launch, and monitor training from your browser.
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[:octicons-arrow-right-24: WebUI Guide](guide/first-experiment.md#webui)
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</div>
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