Fine-tuning Language Models with Generative Adversarial Feedback

Reinforcement Learning with Human Feedback (RLHF) has been demonstrated to significantly enhance the performance of large language models (LLMs) by aligning their outputs with desired human values. However, RLHF is constrained by the expertise and productivity limitations of human evaluators. In this study, we investigate an alternative approach: Reinforcement Learning with Generative Adversarial Feedback (RLGAF) to RLHF. Our preliminary findings indicate that RLGAF can help align LLMs outputs while not suffering from the inherent restrictions of RLHF, suggesting promising avenues for further research on automating AI alignment.