mirror of
https://github.com/microsoft/SkillOpt.git
synced 2026-07-07 08:22:44 +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>
110 lines
3.0 KiB
Markdown
110 lines
3.0 KiB
Markdown
# Configuration Guide
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SkillOpt uses YAML configuration files with a hierarchical override system.
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## Config Structure
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```
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configs/
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├── _base_/
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│ └── default.yaml # Global defaults
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├── searchqa/
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│ └── default.yaml # SearchQA overrides
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├── docvqa/
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│ └── default.yaml # DocVQA overrides
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└── alfworld/
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└── default.yaml # ALFWorld overrides
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```
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Benchmark configs inherit from `_base_/default.yaml` and override specific values.
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## Key Parameters
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### Model
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```yaml
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model:
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backend: azure_openai # azure_openai | openai_chat | claude_code_exec | qwen
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optimizer: gpt-5.5 # Optimizer model (for reflection)
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target: gpt-5.5 # Target model (for rollout)
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```
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### Training
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```yaml
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train:
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num_epochs: 4 # Number of training epochs
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batch_size: 40 # Tasks per step (batch size)
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accumulation: 1 # Gradient accumulation
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seed: 42
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```
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### Gradient (Reflection)
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```yaml
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gradient:
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minibatch_size: 8 # Reflect minibatch size
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analyst_workers: 16 # Parallel reflection workers
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max_analyst_rounds: 3 # Max rounds of analyst reflection
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failure_only: false # Only reflect on failures
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```
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### Optimizer
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```yaml
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optimizer:
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learning_rate: 4 # Max edits per step (edit budget)
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min_learning_rate: 2 # Min edits for decay schedulers
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lr_scheduler: cosine # constant | linear | cosine | autonomous
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use_slow_update: true # Momentum-like blending at epoch boundary
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slow_update_samples: 20 # Samples for slow update evaluation
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use_meta_skill: true # Cross-epoch strategy memory
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```
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### Evaluation
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```yaml
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evaluation:
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use_gate: true # Validation gating (accept/reject updates)
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eval_test: true # Run test evaluation after training
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```
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### Environment (Data)
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```yaml
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env:
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name: searchqa # Benchmark name
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split_mode: ratio # ratio | split_dir
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split_ratio: "2:1:7" # train:val:test ratio
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data_path: "" # Path to dataset
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exec_timeout: 120 # Per-task timeout (seconds)
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```
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## CLI Overrides
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Override any config value from the command line:
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```bash
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python scripts/train.py \
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--config configs/searchqa/default.yaml \
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optimizer.learning_rate=16 \
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optimizer.lr_scheduler=linear \
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gradient.analyst_workers=8
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```
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## Environment Variables
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Model credentials are loaded from environment variables:
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| Variable | Backend | Description |
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| `AZURE_OPENAI_ENDPOINT` | azure_openai | Azure resource endpoint |
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| `AZURE_OPENAI_API_KEY` | azure_openai | Azure API key |
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| `OPENAI_API_KEY` | openai | OpenAI API key |
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| `ANTHROPIC_API_KEY` | claude | Anthropic API key |
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| `QWEN_API_BASE` | qwen | Local Qwen vLLM endpoint |
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## Full Reference
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See [Configuration Reference](../reference/config.md) for the complete parameter list.
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