Verification

Verify execution

1. Check that the connection reports Training API ready.
2. Create the job. Resolve any validation errors before starting.
3. Start training and inspect the job status and progress returned by the API.
4. Check the full API state for reported errors and completed results.
5. Download a checkpoint after a completed training stage.

Verify model quality

For dataset jobs, supply validation data when creating the job. Experience models use generated evaluation when supported. Run validation on the latest checkpoint or an explicit round. Compare metrics on the same held-out dataset. Lower training loss alone does not establish quality on unseen examples.

Training and validation values appear in the checkpoint table and metric chart. The chart uses checkpoint rounds on the horizontal axis; metric direction comes from the model. Test the downloaded checkpoint in your model’s compatible inference tool.

Errors and recovery

Problem	Action
Training API unavailable	Check site registration and widget access. The workspace cannot grant itself permission.
Files rejected	Check model compatibility, manifest entries, original file names, sizes, and checksums.
Job waits for a participant	Check that participant's connection. Use Retry or Stop participant as appropriate.
Checkpoint save fails	Check browser storage. Preserve important downloads and retry saving.
Validation fails	Check model evaluation support and the selected validation file. Read the returned error.

Continue training

Choose a checkpoint, compatible model, manifest, and data files. “New dataset” keeps the weights with a fresh queue. “Same dataset” restores the previous queue. Continuing creates a new job; invite participants again as needed.

Verify reinforcement learning

Check the model’s declared reward or return metric and its direction; metric names are model-defined. Compare generated evaluation on fixed episodes across checkpoint rounds. Training metrics alone do not establish policy quality. Progress uses the model’s declared work units, and a checkpoint marks an aggregated round. Episodes and trajectories stay on participants; only weights, metrics and counts are exchanged.

Recorded answers

Open Help and choose a question. The site calls Recording Demos play(id); the built-in widget repeats actions in the live application. Start from the same page state as the original recording. A missing element stops playback with an error. Stop walkthrough ends playback.

Use Open Recording Demos to create a recording, using its name as the FAQ question and its description as supporting text. Refresh answers to load the shared library. Only saved recordings of kind demo are listed; captured issues are excluded. Libraries are scoped to the exact origin, so recordings on localhost do not automatically appear in production.