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Why does reducing the influence of extra training data ease model-size demands?
Question: In one to three sentences, explain why giving a smaller loss weight to a large auxiliary dataset can let a model with fewer parameters work well.
Sample answer: A smaller loss weight means the optimizer cares less about matching the auxiliary set perfectly, so the network is not forced to allocate as much capacity to fit two objectives equally. It can still learn useful patterns from the extra data without having to be extremely large.
Key points:
- Lower weight eases the conflict between the main task and the auxiliary task.
- Less capacity is needed to satisfy both objectives at once.
- The auxiliary examples still contribute to learning.
Rubric: The answer must explain that lowering the auxiliary loss weight reduces the burden of fitting both data sources and should note that the extra data can still help the model.
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