om Self-Rewarding
LLM alignment compared to the baseline seed model, but importantly the reward modeling
ability, which is no longer fixed, improves as well. This means that the model during iterative
training is able, at a given iteration, to provide a higher quality preference dataset to itself
than in the previous iteration. While this effect likely saturates in real-world settings, it
provides the intriguing possibility of obtaining reward models (and hence LLMs) that are
superior to ones that could hav