Last of all, the GPT-3 is trained with proximal policy optimization (PPO) utilizing rewards to the generated knowledge in the reward model. LLaMA two-Chat [21] increases alignment by dividing reward modeling into helpfulness and safety benefits and working with rejection sampling In combination with PPO. The initial four versions of LLaMA two-Chat