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140 lines
4.3 KiB
Markdown
140 lines
4.3 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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### Skill-Aware Reflection (optional, off by default)
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EmbodiSkill-style failure routing: the failure analyst classifies each
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failure pattern as **SKILL_DEFECT** (the rule is wrong or missing → normal
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gated body edit) or **EXECUTION_LAPSE** (a valid rule exists but was not
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followed → a short reminder appended to a protected appendix region inside
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the skill that step-level edits can never modify).
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```yaml
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optimizer:
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use_skill_aware_reflection: false # Master switch (default off = baseline-identical)
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skill_aware_appendix_source: both # both | failure_only (paper-faithful S_app)
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skill_aware_consolidate_threshold: 0 # >0: LLM-compact the appendix past N notes (experimental)
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```
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Notes:
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- The switch is resolved process-wide from the config
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(`configure_skill_aware_reflection`), so it applies to every benchmark
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with no per-adapter wiring.
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- `failure_only` restricts appendix notes to the failure analyst, matching
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the original S_app formulation; `both` additionally lets the success
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analyst re-emphasize existing rules.
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- Appendix notes bypass the validation gate by design and accumulate with
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order-preserving dedup; lapse-only steps (no body edits) still flush
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their notes.
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- Not supported together with `skill_update_mode=rewrite_from_suggestions`
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or the full-rewrite modes: whole-document rewrites can drop the appendix
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region.
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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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