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- 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
73 lines
3.4 KiB
Markdown
73 lines
3.4 KiB
Markdown
# Configuration Reference
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Complete reference for all SkillOpt configuration parameters.
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## Model
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| Parameter | Type | Default | Description |
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| `model.backend` | str | `azure_openai` | Backend: `azure_openai` / `openai_chat` / `claude_code_exec` / `qwen` |
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| `model.teacher` | str | `gpt-5.5` | Teacher model (for reflection & slow update) |
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| `model.student` | str | `gpt-5.5` | Student model (for rollout execution) |
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| `model.reasoning_effort` | str | `medium` | Reasoning effort level |
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## Training (`train`)
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| Parameter | Type | Default | DL Analogy | Description |
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| `train.num_epochs` | int | 4 | Epochs | Number of training epochs |
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| `train.batch_size` | int | 40 | Batch size | Tasks sampled per step |
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| `train.accumulation` | int | 1 | Gradient accumulation | Accumulation rounds per step |
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| `train.seed` | int | 42 | Random seed | Reproducibility seed |
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## Gradient / Reflection (`gradient`)
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| Parameter | Type | Default | Description |
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| `gradient.minibatch_size` | int | 8 | Reflect minibatch size |
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| `gradient.merge_batch_size` | int | 8 | Patch merge batch size |
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| `gradient.analyst_workers` | int | 16 | Parallel reflection workers |
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| `gradient.max_analyst_rounds` | int | 3 | Max rounds of analyst reflection |
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| `gradient.failure_only` | bool | `false` | Only reflect on failures |
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## Optimizer (`optimizer`)
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| Parameter | Type | Default | DL Analogy | Description |
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| `optimizer.learning_rate` | int | 4 | Learning rate | Max edit patches per step (edit budget) |
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| `optimizer.min_learning_rate` | int | 2 | Min LR | Min edits for decay schedulers |
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| `optimizer.lr_scheduler` | str | `cosine` | LR schedule | `constant` / `linear` / `cosine` / `autonomous` |
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| `optimizer.skill_update_mode` | str | `patch` | — | `patch` / `rewrite_from_suggestions` / `full_rewrite_minibatch` |
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| `optimizer.use_slow_update` | bool | `true` | Momentum | Epoch-boundary longitudinal comparison & guidance |
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| `optimizer.slow_update_samples` | int | 20 | — | Samples for slow update evaluation |
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| `optimizer.use_meta_skill` | bool | `true` | Meta-learning | Cross-epoch teacher-side strategy memory |
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| `optimizer.longitudinal_pair_policy` | str | `mixed` | — | `mixed` / `changed` / `unchanged` |
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## Evaluation (`evaluation`)
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| Parameter | Type | Default | Description |
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| `evaluation.use_gate` | bool | `true` | Enable validation gating (accept/reject updates) |
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| `evaluation.eval_test` | bool | `true` | Run test evaluation after training |
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## Environment (`env`)
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| Parameter | Type | Default | Description |
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| `env.name` | str | — | Benchmark name (e.g., `searchqa`, `docvqa`) |
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| `env.data_path` | str | — | Path to dataset |
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| `env.skill_init` | str | — | Path to initial seed skill (optional) |
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| `env.split_mode` | str | `ratio` | `ratio` or `split_dir` |
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| `env.split_ratio` | str | `2:1:7` | Train:val:test ratio |
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| `env.exec_timeout` | int | 120 | Per-task timeout in seconds |
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| `env.out_root` | str | — | Output directory |
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## Azure OpenAI Credentials
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| Variable | Description |
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| `AZURE_OPENAI_ENDPOINT` / `model.azure_openai_endpoint` | Azure resource endpoint |
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| `AZURE_OPENAI_API_KEY` / `model.azure_openai_api_key` | Azure API key |
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| `OPENAI_API_KEY` | OpenAI API key (for `openai_chat` backend) |
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| `ANTHROPIC_API_KEY` | Anthropic API key (for `claude_code_exec` backend) |
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