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Validate the complete coverage sequence during training #301
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Script executed:
Repository: AOSSIE-Org/OpenVerifiableLLM
Length of output: 3916
Script executed:
Repository: AOSSIE-Org/OpenVerifiableLLM
Length of output: 18819
Script executed:
Repository: AOSSIE-Org/OpenVerifiableLLM
Length of output: 12596
Avoid rescanning all exclusions for each target.
The 16-million-check estimate is accurate for a full 32 × 512 batch and 1,000 exclusion ranges. The changed code performs this work once per update in Python, while the previous implementation scanned the ranges only once per batch. A fork with many excluded pages can therefore add substantial overhead to training and replay.
Walk the sorted ranges once per batch, or maintain a range index while checking the IDs so passed ranges are not rescanned.