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Reinforcement Learning from Human Feedback

Nikita Namjoshi

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:

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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:

1. Explore the two datasets that are used in RLHF training: the “preference” and “prompt” datasets.

2. Use the open source Google Cloud Pipeline Components Library, to fine-tune the Llama 2 model with RLHF.

3. Assess the tuned LLM against the original base model by comparing loss curves and using the “Side-by-Side (SxS)” method.

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What's inside

Syllabus

Traffic lights

Read about what's good
what should give you pause
and possible dealbreakers
Offers hands-on practice with practical examples
Covers advanced topics in reinforcement learning
Instructed by experts in the field of reinforcement learning
Provides opportunities to apply RLHF to real-world scenarios
Course assumes some prior knowledge of reinforcement learning

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Reviews summary

Practical rlhf for llm alignment

According to students, this course offers a highly practical and conceptually solid introduction to Reinforcement Learning from Human Feedback. Learners praise the hands-on labs, particularly the experience of fine-tuning the Llama 2 model using Google Cloud Pipeline Components. While many find the instructor's explanations clear and the assessments valuable, some highlight that the pace can be fast and it helps to have prior familiarity with GCP. It's considered essential for professionals looking to align LLMs.
Well-explained by the instructor, with useful supplementary materials.
"The instructor explains complex topics clearly and makes them approachable."
"Instructor was excellent, making the learning process engaging and effective."
"The side-by-side (SxS) assessment method was a great addition to evaluate the tuned LLM."
Highly relevant and valuable for ML/AI professionals in LLMs.
"This is a must for anyone serious about LLM alignment in a professional context."
"Recommended for ML engineers and researchers looking to deepen their understanding of LLM fine-tuning."
"A foundational course for RLHF, it's a strong start for building expertise in an emerging field."
Offers a strong and clear understanding of RLHF concepts.
"The conceptual understanding of RLHF was solid, providing a great foundation."
"This course completely changed my perspective on LLM fine-tuning. The conceptual clarity was excellent."
"I gained a clear overview of the RLHF training process and its core components."
Provides valuable hands-on experience with LLM fine-tuning.
"The hands-on labs with Llama 2 on GCP were incredibly practical. I immediately applied these techniques to my work."
"Absolutely loved the practical focus. Fine-tuning Llama 2 was insightful and relevant."
"The practical application is valuable; this course provides tools you can immediately use."
Moves quickly, benefiting those with prior Google Cloud experience.
"I found the pace quite fast, especially if you're not already familiar with GCP setup and services."
"The GCP setup was a nightmare for me; it assumes a strong background in Google Cloud, which I didn't have."
"Some parts felt a bit rushed, and I spent more time debugging infrastructure than learning new RLHF concepts."

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Reinforcement Learning from Human Feedback with these activities:
Organize and review course materials
Help you stay organized and focused, enhancing your ability to retain and recall information presented in the course.
Show steps
  • Create a system for organizing your notes, assignments, and other course materials.
  • Review your materials regularly to reinforce your understanding.
  • Summarize key concepts and ideas from the course materials.
Review NLP fundamentals
Help you refresh your knowledge of the underlying technology and concepts of NLP.
Browse courses on NLP
Show steps
  • Read through your notes or textbook chapters on NLP fundamentals.
  • Complete practice problems or quizzes on NLP concepts.
  • Review the course syllabus and identify the key NLP concepts that will be covered.
Follow a tutorial on RLHF
Provide you with a deeper understanding of the RLHF process and its application in aligning LLMs with human values and preferences.
Show steps
  • Identify a comprehensive tutorial or course on RLHF.
  • Follow the tutorial step-by-step, completing the exercises and assignments.
  • Take notes on the key concepts and techniques involved in RLHF.
Two other activities
Expand to see all activities and additional details
Show all five activities
Practice fine-tuning LLMs with RLHF
Allow you to practice the practical application of RLHF in fine-tuning LLMs, giving you hands-on experience with the process.
Show steps
  • Set up your development environment for RLHF.
  • Select a pre-trained LLM and a dataset for RLHF.
  • Implement the RLHF fine-tuning process.
  • Evaluate the fine-tuned LLM against the original base model.
Build a project using a fine-tuned LLM
Help you apply your knowledge of RLHF and LLM fine-tuning to a practical project, demonstrating your understanding and ability to use these techniques.
Show steps
  • Identify a specific use case or problem that can be addressed with a fine-tuned LLM.
  • Design and develop the project using the fine-tuned LLM.
  • Evaluate the performance of the project and make improvements as needed.

Career center

Learners who complete Reinforcement Learning from Human Feedback will develop knowledge and skills that may be useful to these careers:

Reading list

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Course image
Effort
1 hour
Level
Intermediate
Via
Coursera
Institution
DeepLearning.AI
Instructor
Nikita Namjoshi
Language
English
Traffic lights
Read about what's good
what should give you pause
and possible dealbreakers
Offers hands-on practice with practical examples
Covers advanced topics in reinforcement learning
Instructed by experts in the field of reinforcement learning
Provides opportunities to apply RLHF to real-world scenarios
Course assumes some prior knowledge of reinforcement learning
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