tasks/candidate-1890-ml-training/instruction.md

You are an agent, your current working directory is /app.

You can use the tools available to you to interact with the computer to assist the user in completing tasks.

Repair the training-stop lifecycle

You are given a source-focused, offline PyTorch Lightning workspace. A CPU regression driver in /app/run_regression.py exercises a stateful EarlyStopping callback together with the fit loop's minimum-training gates. The current source contains a lifecycle defect: a callback decision made before min_epochs can remain latched and fire later even though the metric has recovered.

Repair the existing source tree; do not replace it with a new evaluator. Your fix must modify both of these production modules:

• vendor/pytorch-lightning/src/lightning/pytorch/callbacks/early_stopping.py
• vendor/pytorch-lightning/src/lightning/pytorch/loops/fit_loop.py

Requirements:

1. A patience/threshold decision made before min_epochs must be deferred, leave trainer.should_stop false, and clear the callback's stopping reason.
2. After the epoch gate is met, ordinary early stopping must still set should_stop, stopped_epoch, and PATIENCE_EXHAUSTED correctly.
3. min_steps is an independent batch-granularity gate. Do not suppress a callback decision merely because min_steps has not been reached.
4. A metric improvement after a deferred decision must reset the wait counter and must not inherit a stale stop decision.
5. Keep the existing framework structure and preserve the distributed boolean reduction call.

Run the supplied CPU regression driver and write the required artifact to /app/output.json. The artifact must retain output_schema_version = tbench.early_stopping.repair.v1 and include both the callback cases and the fit_loop_gates matrix produced by the driver; do not hard-code or hand-edit the expected results.