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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
30 lines
751 B
Python
30 lines
751 B
Python
"""ReflACT: Reflective Agent Tuning.
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A general-purpose framework for iteratively optimizing LLM agent skills
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through structured reflection and self-improvement.
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Pipeline stages:
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1. Rollout — execute episodes with current skill
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2. Reflect — analyze trajectories, generate patches
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3. Aggregate — hierarchical merge of patches
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4. Select — rank and select top edits
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5. Update — apply edits to skill document
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6. Evaluate — validate candidate skill, accept/reject
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"""
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__version__ = "0.1.0"
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from skillopt.types import ( # noqa: F401
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BatchSpec,
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Edit,
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EditOp,
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FailureSummaryEntry,
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GateAction,
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GateResult,
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MetaReflectResult,
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Patch,
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RawPatch,
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RolloutResult,
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SlowUpdateResult,
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)
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