Release native trainer state before cached refinement, rebuild each detector from the persisted EMA checkpoint, save gradient controls incrementally, and reuse hash-verified completed controls after interruption.
Constraint: Preserve baseline checkpoints and fixed optimization budgets
Rejected: Retry candidates in retained trainer process | keeps opaque CUDA state alive
Confidence: high
Scope-risk: moderate
Not-tested: Full three-seed remote development matrix
jung-geun
4b18c0ed5e