dnsod_si666

RWKV looks awesome

You could also use this to measure different models against each other right? And just in general, use this as a model benchmark.

1. Get dataset of text.
2. Tokenize dataset.
3. Measure true probabilities straight from the dataset.
4. Train model number 1 on tokenized dataset.
5. Measure KL divergence of model from true probabilities.
6. Repeat steps 4,5 for model number 2
7. Compare KL divergence of model 1 to model 2.

-Separate Idea- Also isn’t getting the true probabilities useful anyway, because then we could have the training process be:

1. Get dataset.
2. Tokenize.
3. Get true probabilities.
4. Train on probabilities instead of directly on the tokens.

Like instead of training twice (sequence to probabilities):

1. sequence1 -> [1, 0]
2. sequence1 -> [0, 1] You train it once with:
3. sequence1 -> [0.5, 0.5]

So you are training on less data which would reduce training costs and whatnot.

This may be a dumb question, but why do we use any sampling modifications at all? Is that not defeating the purpose of the model training to learn those probabilities?