In fact, this might be related to the famous KL divergence issue — KL penalty is much larger than it should be and the model could pay more attention and optimizes for it more instead, therefore causing negative KL divergence. Larger models get affected more...
In fact, this might be related to the famous KL divergence issue — KL penalty is much larger than it should be and the model could pay more attention and optimizes for it more instead, therefore causing negative KL divergence. Larger models get affected more....
In fact, this might be related to the famous KL divergence issue — KL penalty is much larger than it should be and the model could pay more attention and optimizes for it more instead, therefore causing negative KL divergence. Larger models get affected more. ...
In fact, this might be related to the famous KL divergence issue — KL penalty is much larger than it should be and the model could pay more attention and optimizes for it more instead, therefore causing negative KL divergence. Larger models get affected more. ...
In fact, this might be related to the famous KL divergence issue — KL penalty is much larger than it should be and the model could pay more attention and optimizes for it more instead, therefore causing negative KL divergence. Larger models get affected more...
(2019) mentioned that "to keep the scale of the reward model consistent across training, we normalize it so that it has mean 0 and variance 1 for \( x \sim \mathcal{D}, y \sim \rho(·|x) \).” To perform the normalization process, the code first...