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With the current version of our algorithm, we only do single-hypothesis inference to update their rewards.
Essentially, we could extend the algorithm such that for each training example, we can evaluate a group of hypotheses with some multiple hypotheses inference method, and update the hypotheses accordingly.
This could be beneficial because at downstream inference, we will likely use multiple hypotheses inference instead of using a single one for every test example.
The text was updated successfully, but these errors were encountered:
With the current version of our algorithm, we only do single-hypothesis inference to update their rewards.
Essentially, we could extend the algorithm such that for each training example, we can evaluate a group of hypotheses with some multiple hypotheses inference method, and update the hypotheses accordingly.
This could be beneficial because at downstream inference, we will likely use multiple hypotheses inference instead of using a single one for every test example.
The text was updated successfully, but these errors were encountered: