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eval: support rule-specific value readouts
Testing
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ci(test): cache Linux engine before queued matches
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tuning: support multi-rule policy optimization
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ci(test): cache Linux engine before queued matches
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refactor(tuning): streamline staged model training
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perf(tuning): defer post-layout corpus preparation
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feat(tuning): coalesce curriculum parameter layouts
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feat(tuning): decouple policy logit geometry
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refactor(tuning): keep curriculum parameter layouts uniform
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feat(tuning): coordinate phase-specific objectives
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feat(tuning): support phase-specific curricula
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feat(tuning): support ordered dataset curricula
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feat(tuning): coordinate joint policy objectives
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feat(tuning): support joint value and trace policy training
Testing
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feat(eval): preserve structured classical value summaries
Testing
#303:
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feat(eval): support calibrated classical value readouts
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ci(test): cache Linux engine before queued matches
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feat(eval): support hierarchical classical outcomes
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feat(classical): split quiet and three threat readouts
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feat(classical): add coarse threat-band readout
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feat(classical): add temporal value readout
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feat(classical): add component-aware board readout
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feat(eval): add affine board-size value readout
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feat(classical): add calibrated joint WDL model format
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feat(eval): add board-conditioned value composition
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