 to generate candidate
responses from model Mt, which also predicts its own rewards via LLM-as-a-Judge prompting.
(ii) Instruction following training: preference pairs are selected from the generated data,
which are used for training via DPO, resulting in model Mt+1. This whole procedure can
then be iterated resulting in both improved instruction following and reward modeling ability.
of Self-Instruction creation whereby candidate responses are generated by the model for
newly created prompts, and are then 