duced Self-Rewarding Language Models, models capable of self-alignment via
judging and training on their own generations. The method is trained in an iterative manner,
where in each iteration the model creates its own preference-based instruction training data.
This is done by assigning rewards to its own generations via LLM-as-a-Judge prompting,
and using Iterative DPO to train on the preferences. We showed that this training both
improves the instruction following capability of the model, as well as its r