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A rubric is your team’s written standard for a correct answer. Use one wherever no rule decides correctness: writing quality, tone, house conventions, whether an explanation actually explains. The reason it is an object rather than a prompt is that the same rubric can be both the reward training optimises and the score a benchmark reports. The number going up during training is then the number on the board, not a proxy that drifts away from it.
This page is about using one. To write a good one — criteria, weights, versioning — see writing rubrics.
TunePlane rubrics page

Rubrics with their criteria, versions and usage.

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As a training reward

The direct path is trl/grpo-judge, above: the platform judge scores each completion against the rubric and that score is the reward. For an agent environment, declare it as the environment’s verifier instead:
manifest.json
Either way the platform judge does the scoring. Your training code does not call an LLM itself. Both need TUNEPLANE_JUDGE_ENABLED on the deployment, which is what injects the judge endpoint and token into the job.

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As a benchmark

Declare it in a benchmark pack with the judge runner:
benchmark.yaml
For that runner the rubric is the benchmark’s definition. Swap the rubric and it is a different benchmark, which is why it is a declared field rather than a flag.

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As a safety evaluation

The safety runner uses a rubric to decide whether the model declined what it should have. The suite says which prompts should be refused; the rubric says what counts as refusing. Both belong to your deployment. The platform ships neither, because shipping either would be it declaring a verdict it has no standing to declare.

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Keep training and evaluation on the same standard

The temptation is to train against a rubric and evaluate against a slightly different one, usually because somebody sharpened the training rubric mid-project. Reference the same <owner>/<name> in both and let versioning carry the change.
That lists which runs cited which version, and whether as a reward or as a score. It is the fastest way to answer “were these two runs judged by the same standard”. Editing a rubric increments its version and invalidates the judge’s cached scores for it. Old runs keep pointing at the version they used, and the revision text is kept so you can still read what that version said.

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Check it worked

Resolves the reference the way a job would. If that answers, an environment or a benchmark pack can name it. During training, watch the reward distribution on the Validation tab, not only the mean. A judge-scored reward that collapses to two values means the criteria are not discriminating. That is the rubric needing sharpening, not the model.

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Writing rubrics · Benchmark packs · Agent environments · The Rubrics page