Reinforcement Learning from Human Feedback: Whose Culture, Whose Values, Whose Perspectives?

This approach is partic ularly useful when designing AI systems for tasks where it is difficult to specify a precise reward function or when it is important to align the model's behaviour with certain human expectations and values. For instance, RLHF has notably improved language models for context - aware text generation (Ziegler et al. 2020) and taught robots to navigate cluttered environments (Henry et al. 2010) . RLHF is commonly employed in the later stages of fine - tuning models, particularly in the development of prominent Large Language Models (LLMs) like GPT - 3.5 or GPT - 4. Initially, these models undergo training using vast text corpora to grasp a broad range of language patterns and contexts. This foundational training is supplemented by task - specific fine - tuning, where the models are adjusted to excel in particular applications, such as understanding and generating dialogues. The refinement process is then furt her enhanced through RLHF.