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Relabeling asks a configured model to produce a new answer for every dataset entry. The result is an additive output set: original responses remain available, and multiple relabel runs can coexist.

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When relabeling helps

Relabel when the production model output is not the behavior you want the student to learn, for example:
  • a stronger model can correct weak or inconsistent answers;
  • instructions or structured-output requirements changed;
  • production data contains acceptable inputs but noisy labels;
  • you want to compare two teachers before training.
Skip relabeling when the current outputs are already the desired targets. It adds inference cost and can transfer the teacher’s mistakes or style unnecessarily.

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Run a relabel

Open the task’s Datasets page and select a ready dataset. Select Relabel, choose a model under Relabel with…, then select Relabel dataset. The background job processes entries concurrently and reports queued, running, completed, failed, or stale state. After completion:
  • inspect original and relabeled outputs side by side;
  • filter unchanged or errored rows;
  • select the relabel run as the training output when creating a fine-tune;
  • use it as the primary reference in a head-to-head evaluation.
Deleting a relabel run removes only its generated output set. It does not delete the dataset, original outputs, source traces, or provider.
Replace the dataset ID and choose any registered provider model:
The response contains the relabel run ID and status. See Start a relabel run.
Last modified on August 25, 2026