Reinforcement Learning from Human Feedback (RLHF) is a technique that uses human feedback to train reinforcement learning (RL) agents. In RL, an agent learns to make decisions by interacting with an environment and receiving rewards. RLHF adds an additional step to this process: the agent also receives human feedback on its decisions, which is used to update its policy.

This approach has several advantages over traditional RL, including:

Reduced compounding error: RLHF can help to reduce the problem of compounding error, which is a common issue in RL. This is because human feedback can help the agent to learn from its mistakes and avoid making the same mistakes again.

Improved reward design: RLHF can be used to solve problems where reward engineering is difficult. This is because human feedback can provide more nuanced information about what the agent should do than a simple reward function.

More efficient learning: RLHF can be more efficient than traditional RL, especially for tasks with sparse rewards. This is because human feedback can provide more frequent and informative rewards.

There are two main types of RLHF: offline RLHF and online RLHF. Offline RLHF uses a dataset of human feedback to train the agent, while online RLHF collects human feedback as the agent interacts with the environment.

RLHF has been shown to be effective for a variety of tasks, including question answering, summarization, and dialogue generation. It is a promising new technique with the potential to improve the performance of RL agents.

• Reinforcement Learning in the Era of LLMs
• [2305.18438] Reinforcement Learning with Human Feedback: Learning Dynamic Choices via Pessimism (arxiv.org)
• [2204.05862] Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback (arxiv.org)
• Illustrating Reinforcement Learning from Human Feedback (RLHF) (huggingface.co)
• [2307.04964] Secrets of RLHF in Large Language Models Part I: PPO (arxiv.org)
• [2401.06080] Secrets of RLHF in Large Language Models Part II: Reward Modeling (arxiv.org)