What role does RLHF play in the training of large models?

RLHF (Reinforcement Learning from Human Feedback) plays a crucial role in aligning large language model outputs with human values and preferences after initial pre-training and fine-tuning. Its core function is to bridge the gap between raw model capabilities and desirable, safe, helpful responses.

This alignment is achieved through reinforcement learning. Human evaluators rank or rate different model outputs for prompts, creating a dataset of human preferences. This dataset trains a separate Reward Model to predict which outputs humans would prefer. The main large model is then fine-tuned using the reward model's predictions as a reward signal, iteratively optimizing its policy to generate higher-scoring outputs more aligned with human judgment. RLHF is vital for refining coherence, relevance, safety, and helpfulness.

To implement RLHF, key steps are: collecting human preference data on model outputs, training a reward model to predict these preferences, and then fine-tuning the main model using RL algorithms like Proximal Policy Optimization (PPO) guided by the reward model. This process significantly enhances the model's performance in real-world applications, such as chatbots and content creation tools, by producing more contextually appropriate, accurate, and harmless responses desired by users.