Self-Rewarding Language Models

Understand

We posit that to achieve superhuman agents, future models require superhuman feedback in order to provide an adequate training signal.

• Current approaches commonly train reward models from human preferences, which may then be bottlenecked by human performance level, and secondly these separate frozen reward models cannot then learn to improve during LLM training.
• In this work, we study Self-Rewarding Language Models, where the language model itself is used via LLM-as-a-Judge prompting to provide its own rewards during training.
• We show that during Iterative DPO training that not only does instruction following ability improve, but also the ability to provide high-quality rewards to itself.