Reinforcement learning from human feedback

Reinforcement learning from human feedback (RLHF) is a subfield of reinforcement learning (RL) that trains agents using human feedback as reinforcement signals. In RL, agents interact with an environment, collect rewards or punishments based on actions taken and adjust their behavior to maximize rewards. However, designing accurate reward functions or annotating sufficient data for this purpose is difficult in many real-world scenarios.

RLHF addresses this limitation by incorporating human feedback directly into the learning process, improving the efficiency of training, optimizing output towards human preferences and providing a more understandable learning process. Typically, the reward signal is replaced with feedback from human teachers or users, provided through labels, rankings, or binary responses, often provided through an intermediate preference model trained on human feedback.

Challenges

Handling noise and variance in human feedback is a key challenge in RLHF, as human teachers may vary in expertise or opinions, leading to inconsistent feedback. Feedback may also be noisy due to miscommunication or mistakes.

Despite these challenges, RLHF has shown promise in personalized recommendation systems, game playing, and dialog systems, among other applications. RLHF is a growing field with significant potential to improve the training and performance of RL agents in a variety of real-world settings.