Why would you deliberately train on examples where the...

If every training context contains the answer, the model learns a zero-loss policy of simply copying from the context. This leaves the model without a signal for cases where the context is insufficient, leading to failure. By including examples where the answer is not in the context, the model learns to rely on its internal weights to provide the correct answer, effectively learning to override the context when appropriate.