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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
9 lines
283 B
Python
9 lines
283 B
Python
"""ReflACT Scheduler -- edit budget and learning rate scheduling.
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Analogous to learning rate schedulers (cosine annealing, step decay, warmup)
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in neural network training. Controls how the edit_budget evolves over the
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course of training.
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Placeholder for future implementations.
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"""
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