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Use the job ID returned by training or explicit preparation. Read get_job and follow its operation resource; inspect_training_progress provides detailed development evidence. Use the job returned by training. In a new conversation, provide the job link or ID. What you get: A report from stored progress and checks, without starting another job. The steps below describe the workflow your agent follows and the results to verify.

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Identify what is happening

Read the current stage and its measured counters. Transfer, runtime initialisation, weight loading, adapter setup, tokenizer verification, dataset construction, training, validation and checkpoint verification can report separate progress. Compare three clocks:
  • Observation: when the platform last read a state.
  • Heartbeat: when the worker last reported being alive.
  • Forward progress: when the worker last advanced measured work.
A fresh heartbeat alone does not establish that weight loading or an optimizer step advanced. Missing loader counters cannot produce a measured percentage.

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Inspect development evidence

Call inspect_training_progress(job=...) and follow the returned check or checkpoint identities. Use check for retained examples and probe for frozen sample identities. When an overview returns collections, pass its field JSON Pointer with offset and limit to retrieve the exact entries. check, probe and field are mutually exclusive inspection modes; send only one per call. Read loss, coverage, invalid predictions, check failures and checkpoint reload verification separately. For classification, inspect the majority-label comparison on the same scored subset. Missing predictions for a represented label are evidence to investigate; they are not a diagnosis of the cause. Detailed examples may still be collecting after a check completes. A pending evidence download does not mean the trainer is still running. Inspection reads stored receipts and does not wake a worker.

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Decide whether to stop

Use cancel_finetune to record cancellation intent before the provider request. Inspect acknowledgement and terminal state. Retained checks and checkpoints remain useful evidence even if training is cancelled. Do not assume that a reloadable adapter supports full optimizer continuation. Use only the recovery capabilities exposed by that job and runtime. If provider acknowledgement is unknown, inspect its saved call identity instead of launching another trainer. A completed training job can still have a failed deployment or incomplete final comparison. Report each outcome separately. See Training reference and Recover interrupted work.