CharlesYang030
244e346b83
- 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
46 lines
2.3 KiB
YAML
46 lines
2.3 KiB
YAML
# ──────────────────────────────────────────────────
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# SkillOpt Config Template — <Your Benchmark Name>
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# ──────────────────────────────────────────────────
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# Copy this file to configs/<your_benchmark>/default.yaml
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# and customize the values below.
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# Inherit global defaults
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_base_: ['../_base_/default.yaml']
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# ── Environment ──────────────────────────────────
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env:
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name: your_benchmark # Must match registry key
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data_path: data/your_benchmark # Path to your data
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split_mode: ratio # "ratio" or "split_dir"
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split_ratio: "2:1:7" # train:val:test
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exec_timeout: 120 # Per-task timeout (seconds)
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# ── Training ─────────────────────────────────────
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train:
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num_epochs: 4 # Number of epochs
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batch_size: 40 # Tasks per step (batch size)
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seed: 42
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# ── Gradient (Reflection) ───────────────────────
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gradient:
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analyst_workers: 16 # Parallel reflection workers
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minibatch_size: 8
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# ── Optimizer ────────────────────────────────────
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optimizer:
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learning_rate: 4 # Max edits per step (edit budget)
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lr_scheduler: cosine # cosine | linear | constant | autonomous
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use_slow_update: true # Epoch-boundary momentum
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use_meta_skill: true # Cross-epoch teacher memory
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# ── Evaluation ───────────────────────────────────
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evaluation:
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use_gate: true # Validation gating
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eval_test: true # Run test eval after training
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# ── Model ────────────────────────────────────────
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model:
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backend: azure_openai # azure_openai | openai_chat | claude_code_exec | qwen
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teacher: gpt-5.5
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student: gpt-5.5
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