SkillOpt v0.1.0: initial release

- Skill optimization framework with training loop analogy - 11 benchmarks, 4 model backends (Azure OpenAI, Claude, Codex, Qwen) - WebUI for browser-based training control - Pluggable architecture for extending benchmarks and backends
2026-05-21 17:22:04 +00:00
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+# Configuration Guide
+
+SkillOpt uses YAML configuration files with a hierarchical override system.
+
+## Config Structure
+
+```
+configs/
+├── _base_/
+│   └── default.yaml          # Global defaults
+├── searchqa/
+│   └── default.yaml          # SearchQA overrides
+├── docvqa/
+│   └── default.yaml          # DocVQA overrides
+└── alfworld/
+    └── default.yaml          # ALFWorld overrides
+```
+
+Benchmark configs inherit from `_base_/default.yaml` and override specific values.
+
+## Key Parameters
+
+### Model
+
+```yaml
+model:
+  backend: azure_openai          # azure_openai | openai_chat | claude_code_exec | qwen
+  teacher: gpt-5.5               # Teacher model (for reflection)
+  student: gpt-5.5               # Student model (for rollout)
+```
+
+### Training
+
+```yaml
+train:
+  num_epochs: 4                  # Number of training epochs
+  batch_size: 40                 # Tasks per step (batch size)
+  accumulation: 1                # Gradient accumulation
+  seed: 42
+```
+
+### Gradient (Reflection)
+
+```yaml
+gradient:
+  minibatch_size: 8              # Reflect minibatch size
+  analyst_workers: 16            # Parallel reflection workers
+  max_analyst_rounds: 3          # Max rounds of analyst reflection
+  failure_only: false            # Only reflect on failures
+```
+
+### Optimizer
+
+```yaml
+optimizer:
+  learning_rate: 4               # Max edits per step (edit budget)
+  min_learning_rate: 2           # Min edits for decay schedulers
+  lr_scheduler: cosine           # constant | linear | cosine | autonomous
+  use_slow_update: true          # Momentum-like blending at epoch boundary
+  slow_update_samples: 20        # Samples for slow update evaluation
+  use_meta_skill: true           # Cross-epoch strategy memory
+```
+
+### Evaluation
+
+```yaml
+evaluation:
+  use_gate: true                 # Validation gating (accept/reject updates)
+  eval_test: true                # Run test evaluation after training
+```
+
+### Environment (Data)
+
+```yaml
+env:
+  name: searchqa                 # Benchmark name
+  split_mode: ratio              # ratio | split_dir
+  split_ratio: "2:1:7"           # train:val:test ratio
+  data_path: ""                  # Path to dataset
+  exec_timeout: 120              # Per-task timeout (seconds)
+```
+
+## CLI Overrides
+
+Override any config value from the command line:
+
+```bash
+python scripts/train.py \
+  --config configs/searchqa/default.yaml \
+  optimizer.learning_rate=16 \
+  optimizer.lr_scheduler=linear \
+  gradient.analyst_workers=8
+```
+
+## Environment Variables
+
+Model credentials are loaded from environment variables:
+
+| Variable | Backend | Description |
+|---|---|---|
+| `AZURE_OPENAI_ENDPOINT` | azure_openai | Azure resource endpoint |
+| `AZURE_OPENAI_API_KEY` | azure_openai | Azure API key |
+| `OPENAI_API_KEY` | openai | OpenAI API key |
+| `ANTHROPIC_API_KEY` | claude | Anthropic API key |
+| `QWEN_API_BASE` | qwen | Local Qwen vLLM endpoint |
+
+## Full Reference
+
+See [Configuration Reference](../reference/config.md) for the complete parameter list.