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FreeReinforcement Learning from Human Feedback
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Base language models learn from human-written text, but they are not automatically aligned with the values or preferences you want. This course explains how Reinforcement Learning from Human Feedback closes that gap and why it is currently the main method used for alignment.
Learners with intermediate Python who want hands-on experience with a preference-tuning technique will get the most out of this. The conceptual part comes first, so the practice has a clear frame.
Large language models (LLMs) are trained on human-generated text, but additional methods are needed to align an LLM with human values and preferences.
Reinforcement Learning from Human Feedback (RLHF) is currently the main method for aligning LLMs with human values and preferences. RLHF is also used for further tuning a base LLM to align with values and preferences that are specific to your use case.
In this course, you will gain a conceptual understanding of the RLHF training process, and then practice applying RLHF to tune an LLM. You will:
Who should join?
Anyone with intermediate Python knowledge who’s interested in learning about using the Reinforcement Learning from Human Feedback technique.
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