data pipeline

Distinguish stale telemetry, skipped updates and a real learning plateau using counters, parameter changes and a fixed evaluation batch.

Build a training schedule from explicit model, token, state and recovery assumptions. Keep active compute time separate from availability and contingency.

Version teacher outputs, separate learning from evaluation, and catch a release whose domain score improves while a protected capability regresses.

A 128,000-ID vocabulary breaks the 16-bit storage estimate. Follow the bytes from raw ingestion to 60 TB of token payload, then design deduplication, manifests and a restartable loader.

Trace event time and serving availability through a late-arriving feature. Build an executable historical join and keep online freshness separate from training correctness.