They’re not trained on “next pixel prediction”, hence they don’t have likelihoods on tap
Empirically they learn an approximation to the derivative of the likelihood (Ie the score function). So we have a bound on the true likelihood distribution, but don’t have exact likelihoods (which we do have in LLMs).
In practice this bound is too loose to be used for train with GRPO effectively (though folks have tried).
Empirically they learn an approximation to the derivative of the likelihood (Ie the score function). So we have a bound on the true likelihood distribution, but don’t have exact likelihoods (which we do have in LLMs).
In practice this bound is too loose to be used for train with GRPO effectively (though folks have tried).