mirror of
https://github.com/microsoft/SkillOpt.git
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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>
111 lines
2.4 KiB
Markdown
111 lines
2.4 KiB
Markdown
# Your First Experiment
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This guide walks through running a complete SkillOpt training on SearchQA.
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## 1. Choose a Benchmark
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SkillOpt includes ready-to-use configs for several benchmarks:
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| Benchmark | Difficulty | Typical Runtime |
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|---|---|---|
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| SearchQA | ⭐ Easy | ~30 min |
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| DocVQA | ⭐⭐ Medium | ~2 hours |
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| ALFWorld | ⭐⭐⭐ Hard | ~3 hours |
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We'll use **SearchQA** as it's the fastest to complete.
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## 2. Configure
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Review the config file:
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```bash
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cat configs/searchqa/default.yaml
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```
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Key parameters (deep learning analogy in parentheses):
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```yaml
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train:
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num_epochs: 4 # (epochs)
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batch_size: 40 # (batch size)
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optimizer:
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learning_rate: 4 # (max edits per step)
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lr_scheduler: cosine # (learning rate schedule)
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use_slow_update: true # (momentum at epoch boundary)
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use_meta_skill: true # (cross-epoch optimizer memory)
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gradient:
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analyst_workers: 16 # (parallel reflection workers)
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evaluation:
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use_gate: true # (validation gating)
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```
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## 3. Train
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```bash
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python scripts/train.py --config configs/searchqa/default.yaml
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```
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You'll see output like:
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```
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[Step 1/8] Rollout: 20 items, 4 workers...
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[Step 1/8] Score: 0.65 → Reflect...
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[Step 1/8] 6 edit patches generated
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[Step 1/8] Selected 4 edits (lr=8, cosine → 7.7)
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[Step 1/8] Gate: val score 0.68 > 0.65 ✓ ACCEPT
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[Step 2/8] ...
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```
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## 4. Monitor
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Training outputs are saved to `outputs/<benchmark>/<run_id>/`:
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```
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outputs/searchqa/2024-01-15_10-30-00/
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├── steps/
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│ ├── step_0001/
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│ │ ├── candidate_skill.md
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│ │ ├── step_record.json
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│ │ └── trajectory_digest.json
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│ └── step_0002/
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├── slow_update/
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│ └── epoch_02/
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├── meta_skill/
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│ └── epoch_02/
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├── skills/
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│ └── step_0001.md
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├── best_skill.md
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├── history.json
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└── config.yaml
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```
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## 5. Evaluate
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Evaluate the best skill on the test split:
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```bash
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python scripts/eval_only.py \
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--config configs/searchqa/default.yaml \
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--skill outputs/searchqa/<run_id>/skills/best_skill.md
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```
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## WebUI
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Prefer a graphical interface? Launch the WebUI:
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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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Then open `http://localhost:7860` in your browser to configure parameters and launch training.
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## Next Steps
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- [Understand the training loop](training-loop.md)
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- [Configuration reference](../reference/config.md)
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- [Add a new benchmark](new-benchmark.md)
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