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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
16 lines
628 B
Python
16 lines
628 B
Python
"""ReflACT Optimizer -- skill update operations.
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Analogous to the optimizer in neural network training: applies the computed
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"gradient" (patches) to the current skill document to produce an updated
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candidate skill.
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Modules
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-------
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- skill: edit application (optimizer.step() / parameter update)
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- clip: edit ranking and selection (gradient clipping)
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- meta_reflect: epoch-level macro refinement (momentum)
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- slow_update: longitudinal comparison and guidance (EMA / regularization)
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"""
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from skillopt.optimizer.skill import apply_edit, apply_patch # noqa: F401
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from skillopt.optimizer.clip import rank_and_select # noqa: F401
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