NCA-GENL Experimentation Practice Question

Answer analysis

Option-by-option breakdown

For each option: why learners choose it and why it is or isn't the right answer here.

• ✗

  Stopping training after a fixed number of days

  Why it's wrong here

  Stopping training based on time is an arbitrary constraint that ignores the model's actual performance. A model might need more or less time depending on the learning rate and data. Stop-loss techniques should be dynamic and based on performance thresholds, not on a hard-coded time limit that ignores convergence status.

• ✓

  Terminating training if loss does not improve

  Why this is correct

  This is the definition of a stop-loss strategy. By setting a patience threshold for metric improvement, you can programmatically kill experiments that have stalled, effectively managing cloud spend and compute availability. It ensures that GPU resources are utilized only for runs that demonstrate potential for reaching the desired performance targets.

• ✗

  Increasing the learning rate when loss is high

  Why it's wrong here

  This is a reactive optimization strategy, not a stop-loss technique. While it aims to improve convergence, it does not stop the experiment. Stop-loss is specifically concerned with the termination of ineffective experiments to preserve resources, rather than the manual adjustment of hyperparameters during the training process itself.

• ✗

  Saving a checkpoint every 100 iterations

  Why it's wrong here

  Saving checkpoints is a fault-tolerance strategy, not a stop-loss technique. It allows for recovery in case of hardware failure or for comparing model states, but it does not proactively stop an experiment. These are distinct operational concepts, and confusing them can lead to significant waste of compute resources.