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Open any job from the job list. Eight tabs, each answering a different question.
Job charts tab

Charts: reward, accuracy and training scalars, step by step.

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The tabs

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Charts

Which curves appear is not a console decision — the recipe declares them in its metric contract, and the first two go on the overview chart. That is why two methods show different defaults and why a framework rename does not silently blank a chart.
Empty charts with moving logs means reporting is not happening, not that training is broken. See reporting from training code.

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Validation

Job validation tab

Validation samples with rewards, and the reward distribution.

Sample conversations with their rewards, plus the distribution. Algorithm-specific fields — a DPO rejected completion, an SFT reference answer — appear alongside without the platform needing to know what they are. Reading the histogram is often faster than reading the reward curve: a mean that is climbing while the distribution splits into two clumps usually means the model found one easy subset and stopped improving on the rest.

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Diagnosis

Job diagnosis tab

Diagnosis: findings with confidence, and a proposed follow-up.

Runs automatically on failure and on a schedule while training, if the deployment enables it. Each finding carries a confidence level, and a proposal may come with a submit plan — a concrete follow-up job you approve or reject.
Approving a plan submits a real training job and spends real quota. The dialog says so; read the diff before approving.

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What you can do