## TL;DR
Fix the metric direction in the tracker configuration, then re-pick the best checkpoint: the checkpoint marked best has worse validation loss because higher-is-better was applied to a loss, so correct the goal direction and rescan the run checkpoints. Best is a comparison, and the comparison used the wrong sign. The checkpoints themselves are fine; the label landed on the wrong one.

```text
checkpoint marked best has worse validation loss after metric direction flipped error
```

## Use this when
- The check or run fails with this symptom: a checkpoint marked best has worse validation loss after the metric direction flipped.
- You are working on an experiment workflow using automatic best checkpoint selection on a validation loss and the failure repeats on the same data or configuration.
- You need the failing run, call, or stage isolated before changing code or thresholds.

## Not for this skill when
- A run where the worse loss checkpoint is genuinely preferable for another metric, which should be recorded as a deliberate tradeoff, not a best label.
- The error text names a different tool, stage, or root cause. Match the verbatim error first and use that skill.

## Steps
1. Read the metric goal or direction setting for the run or sweep. Verify: the direction for the loss metric is set to maximise, or unset and defaulted wrongly
2. List the checkpoints with their validation losses. Verify: the true minimum loss checkpoint is identified
3. Correct the direction for the metric in the tracker and in the training template. Verify: the setting now treats lower loss as better
4. Re-run the best selection over the existing checkpoints. Verify: the best label moves to the minimum loss checkpoint
5. Verify the promoted model artifact matches the newly selected checkpoint. Verify: the artifact checksum equals that checkpoint

## Variant phrasings
### checkpoint best worse validation loss metric direction flipped
The shorter phrasing focused on the named failure.
### best checkpoint selection wrong direction error
The symptom phrasing a searcher tries next.
### metric direction loss best checkpoint fix
The variant naming the failing stage or tool directly.

Compatibility: W&B metric goals, MLflow model selection, training framework checkpoint callbacks

## Why it happens
Metric direction is configuration that lives in several places: the training callback, the tracker run, and the sweep definition. A flip in any one, often during a refactor from an accuracy to a loss objective, inverts selection while every number stays plausible.

## Edge cases / pitfalls
- Direction defaults differ per tool and per metric name; set it explicitly for every primary metric.
- Historical runs keep the wrong best label until reselected; a configuration fix alone does not repair old selections.

## Provenance

Resolved from the public thread: https://vectle.com/posts/pst_RvW4YBol7MIWKbunH9GqNw