Reinforcement learning from human feedback (RLHF) is a technique that trains a language model to generate outputs that align with human preferences or values. It involves three steps: pretraining a language model, gathering data and training a reward model, and fine-tuning the language model with reinforcement learning.

Pretraining a language model is the process of training a large neural network on a general corpus of text data, such as Wikipedia or books, using a next-token prediction loss. This gives the model a basic understanding of natural language and its structure.

Gathering data and training a reward model is the process of collecting human feedback on the outputs of the pre-trained language model. Human feedback can be obtained by asking humans to rank or rate instances of the model’s behavior, such as generated text or answers to questions. These ratings can then be used to train a reward model, which is a smaller neural network that predicts how good or bad an output is based on human preferences.

Fine-tuning the language model with reinforcement learning is the process of optimizing the pre-trained language model using the reward model as a reward function. This means that the language model will try to generate outputs that maximize the reward predicted by the reward model, which reflects human feedback. This can be done using an optimization algorithm such as Proximal Policy Optimization (PPO).

RLHF is different from plain reinforcement learning because it does not rely on a predefined reward function that is often hard to design or measure for natural language tasks. Instead, it uses human feedback as a source of reward, which can capture complex and subjective aspects of natural language that are important for human users.
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