WEBVTT

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Post-training changes what a model is likely to do. An input creates a distribution over possible actions, evaluation supplies evidence, and an optimizer changes that distribution.

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Here a cancellation test is failing. The policy assigns chances to return None, raise the cancellation error, or retry. Those probabilities describe the available behavior; sampling produces one actual patch.

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Loss is a number saying how bad behavior looks under the training rule. Useful training lowers both training loss and held-out error.

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If training loss falls while held-out error rises, the model is learning the wrong thing.

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The sampled patch passes. Training makes the exception-raising action slightly easier to choose in similar states. Ordinary backpropagation implements the change; reinforcement learning determines which sampled behavior receives credit.

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A usable training record must also identify who generated the attempt and what evidence accompanied it.