Reinforcement learning from human feedback

1. Initial phase—selecting anphase—An existing model is selected to determine and label correct behavior. Using a pre-trained model saves time due to the significant amount of data required for training.
2. Human feedback—afterfeedback—After training the existing model, human testers provide feedback on performance, providing a quality or accuracy score to model-generated outputs. The model then evaluates its performance based on human feedback to create an improved reward function for reinforcement learning.
3. Reinforcement learning—thelearning—The reward model is fine-tuned based on outputs from the main model and quality scores from testers. The main model then applies this feedback to enhance its performance on future tasks.

RLHF is an iterative process, with additional human feedback and model refinement for continuous improvement. However, there are also challenges and limitations to implementing RLHF, including:

• Subjectivity and human error—feedbackerror—Feedback quality can vary between users and testers. For example, when generating answers, only testers with the proper background in complex fields, such as science or medicine, should provide feedback.
• Scalability—incorporating humanScalability—Human feedback increasesis incorporated, increasing the time and cost of training, which potentially limitinglimits scalability.

Reinforcement learning from human feedback (RLHF) is a machine learning technique that combines methods from reinforcement learning, such as reward functions, with human guidance to train an AI model. Incorporating human feedback into reinforcement learning helps produce AI models capable of performing tasks more aligned with human goals.

RLHF is used across generative artificial intelligence (generative AI) applications, in particular natural language processing (NLP) models and large language models (LLMs), improving the understanding of AI agents in applications such as chatbots, conversational agents, and text-to-speech generation and summarization. RLHF incorporates human testers and users to provide direct feedback to enhance language model performance over self-training alone, making AI-generated text more efficient, logical, and helpful to the user.

Traditional reinforcement learning uses self-training with AI agents learning from a reward function that varies based on their actions. However, it can be difficult to define the reward function, especially for complex tasks such as NLP. RLHF training can be divided into three phases:

1. Initial phase—selecting an existing model to determine and label correct behavior. Using a pre-trained model saves time due to the significant amount of data required for training.
2. Human feedback—after training the existing model, human testers provide feedback on performance, providing a quality or accuracy score to model-generated outputs. The model then evaluates its performance based on human feedback to create an improved reward function for reinforcement learning.
3. Reinforcement learning—the reward model is fine-tuned based on outputs from the main model and quality scores from testers. The main model then applies this feedback to enhance its performance on future tasks.

RLHF is an iterative process, with additional human feedback and model refinement for continuous improvement. However, there are also challenges and limitations to implementing RLHF, including:

• Subjectivity and human error—feedback quality can vary between users and testers. For example, when generating answers only testers with the proper background in complex fields, such as science or medicine, should provide feedback.
• Wording of questions—AI agents can become confused depending on the question's wording used in training.
• Training bias—RLHF can have problems with machine learning bias, in particular for more complex questions or those that are political or philosophical in nature.
• Scalability—incorporating human feedback increases the time and cost of training potentially limiting scalability.