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What is RLHF in LLMs?

RLHF stands for Reinforcement Learning with Human Feedback. It is a technique used to fine-tune Large Language Models (LLMs) like OpenAI's GPT series to make their outputs more aligned with human preferences, values, and expectations. RLHF is a critical step in improving the safety, usefulness, and reliability of LLMs in real-world applications.

Here’s how RLHF works:

  1. Supervised Fine-Tuning:
    Initially, the LLM is fine-tuned on a dataset where human annotators provide high-quality responses to prompts. This helps the model learn better patterns and behaviors.

  2. Reward Model Training:
    Human annotators rank multiple outputs generated by the model for the same input based on quality, relevance, and alignment with human values. These rankings are used to train a reward model, which learns to predict human preferences.

  3. Reinforcement Learning:
    The LLM is further fine-tuned using reinforcement learning, where the reward model provides feedback on the quality of the generated text. The model learns to maximize the reward, effectively aligning its outputs with human preferences.

RLHF has been instrumental in making LLMs like ChatGPT more conversational, accurate, and safe. It helps reduce harmful or biased outputs and ensures the model behaves in ways that are more useful and aligned with user expectations.


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