Which reinforcement learning algorithm is typically utilized during the RL Fine-Tuning stage of RLHF?
Answer
Proximal Policy Optimization (PPO)
During the third stage of Reinforcement Learning from Human Feedback (RLHF), the fine-tuning of the original large language model is typically executed using the Proximal Policy Optimization (PPO) algorithm. PPO is favored in this context because it offers a good balance of stability and sample efficiency compared to some older policy gradient methods. In this step, the LLM acts as the policy, and it is updated based on the reward signals provided by the previously trained Reward Model (RM), effectively steering the model's output generation capabilities toward maximizing human preference scores.
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