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Fix fully_async stale-drop count and carry-over execution mode (P0-S follow-up) - #257
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Miles resets _metric_stale_groups in get_metrics right after the trained-groups hook, so the value is already per round; subtracting the previous reading turned 9, 0, 10, 0 into 9, -9, 10, -10. Report it as read. Rounds whose rollout overlaps training (Miles and verl fully_async) now label rl_rollout_carry_over with execution_mode "partitioned-overlap"; the profile keeps its partitioned-serial step-scheduling mode, and the driver lets carry-over fields replace the profile label for that event. Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
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Summary
Two fixes to the P0-S rollout/training overlap from #245, found during review of the P0-S GPU evidence, plus the golden samples that the first fix invalidates.
What changed
fully_async_prompts.py: Miles resets_metric_stale_groupsinget_metricsright after the trained-groups hook, so the value is already per round. Subtracting the previous reading turned 9, 0, 10, 0 into 9, -9, 10, -10. The value is now reported as read.rl_rollout_carry_overwith execution modepartitioned-overlap. The profile keeps itspartitioned-serialstep-scheduling mode; the driver lets carry-over fields replace the profile label for that event only.rollout_meta_hook.pychanged, so its source hash changed: 8 decoupling golden samples are regenerated, with a hash-migration record.Behavior changes / compatibility
rl_rollout_carry_over.execution_modeispartitioned-overlapfor fully_async rounds (waspartitioned-serial).Tests
Depends on
#256. Branch rebased with no tree change; the GPU code baseline
s23-p0s-fix@7a8485e5has the same tree.Not verified on GPU
Not run on GPU yet. The S23 GPU runs (third stage) use this branch as their code baseline.
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