You signed in with another tab or window. Reload to refresh your session.You signed out in another tab or window. Reload to refresh your session.You switched accounts on another tab or window. Reload to refresh your session.Dismiss alert
If I understand the tree_search algorithm right, the dynamic programming process should be able to find the optimal number of generated tokens according to the acceptance-rate-vector. Also, given the acceptance-rate-vector and the candidate tree, the number of generated tokens can also be computed. But this is just theory. In the paper, the number of generated tokens are measured with experimenting runs. I'm wondering if these experimental-measured generated token numbers agree with the theoretical optimal generated token number?
I was trying to verify it, but in the repo, there is only tree_maps, while the acceptance vectors are missing. I'm wondering if you have considered this estimation before. Or, could you share the acceptance vectors, so that, along with the corresponding trees, I can quickly verify it?
Thanks!
The text was updated successfully, but these errors were encountered:
Hi,
If I understand the tree_search algorithm right, the dynamic programming process should be able to find the optimal number of generated tokens according to the acceptance-rate-vector. Also, given the acceptance-rate-vector and the candidate tree, the number of generated tokens can also be computed. But this is just theory. In the paper, the number of generated tokens are measured with experimenting runs. I'm wondering if these experimental-measured generated token numbers agree with the theoretical optimal generated token number?
I was trying to verify it, but in the repo, there is only tree_maps, while the acceptance vectors are missing. I'm wondering if you have considered this estimation before. Or, could you share the acceptance vectors, so that, along with the corresponding trees, I can quickly verify it?
Thanks!
The text was updated successfully, but these errors were encountered: