Incremental learning releases: checkpoint replay and forgetting gates

Last updated: 6 Oct 20265 min read

tutorial

An updated model state must be reproducible from its parent checkpoint and admitted events before promotion.

Checkpoint a closed update window

At the end of an approved warehouse shift window, freeze the parent model digest, ordered event IDs, label revisions, feature transform and update code. Write a candidate checkpoint to a new immutable path, never over the current serving artifact. Record the applied-event watermark and an integrity digest for the event list. If a worker dies halfway through, restart from the last committed checkpoint and replay the window. Checkpoint recovery covers training interruptions; incremental updates add duplicate and correction semantics.

Test retained and new behavior

A learner that adapts to a new warehouse can forget older facilities. Evaluate the candidate on a recent mature window and a pinned retention suite covering established sites, holidays and rare staff shortages. Compare against the current serving model on the same frames. Monitor underforecast cost and overstaffing, not only average error. Slice gates protect an older facility whose performance regresses while the global metric improves.

Publish through a separate pointer

The candidate state remains offline until evaluation, artifact integrity and serving-compatibility checks pass. Promote by changing a versioned pointer, then canary by facility. If quality or capacity falls, restore the prior pointer; keep the candidate and event ledger for diagnosis. Do not attempt to reverse individual gradient updates in a generic learner when its update operation has no valid inverse. A reversible pointer is simpler and testable.

Handle corrected labels honestly

If a label revision changes inside a committed window, create a new candidate from the last clean parent and replay corrected events in the defined order. Preserve both checkpoint lineages and note which served decisions used the old state. A replay may produce different bytes if update order, seeds or runtime change, so pin them and allow only a documented numeric tolerance. The project catches a correction that a naive second update would double count.

Implementation

def incremental_promotion(candidate, incumbent, limits):
    if candidate["parent_digest"] != incumbent["artifact_digest"]:
        return "hold:wrong-parent"
    if not candidate["replay_complete"] or candidate["duplicate_events"]:
        return "hold:replay-integrity"
    if candidate["old_site_error"] > limits["old_site_error"]:
        return "hold:forgetting"
    if candidate["new_site_error"] > limits["new_site_error"]:
        return "hold:new-site-quality"
    return "canary:versioned-pointer"

incumbent = {"artifact_digest": "staff-r47"}
limits = {"old_site_error": 0.18, "new_site_error": 0.22}
candidate = {"parent_digest": "staff-r47", "replay_complete": True,
             "duplicate_events": 0, "old_site_error": 0.24,
             "new_site_error": 0.16}
assert incremental_promotion(candidate, incumbent, limits) == "hold:forgetting"
assert incremental_promotion({**candidate, "old_site_error": 0.15},
                             incumbent, limits) == "canary:versioned-pointer"

Performance and operating cost

The aggregate gate is O(1) time and space; a replay from a clean checkpoint costs O(u) update work for u admitted events. Keeping an older-facility retention suite and immutable checkpoints uses storage and evaluation compute, but it makes catastrophic forgetting and rollback visible before every warehouse is exposed.

Common Mistakes

Overwriting the serving checkpoint in place.
Testing only the newest warehouse and missing older-site regression.
Applying a corrected label as an extra update to a nonreversible learner.
Claiming deterministic replay without pinning event order and runtime.