@NFTCPS: If you work in AI, take this UCLA course! Theory + practice: a deep dive into RL and LLM training from scratch. Covers MDP, PPO algorithms, the full RLHF process, and hands-on Jupyter coding. Taught by a UCLA professor with videos and assignments, ready to apply immediately after completion. Course URL: https://ernestryu.com/courses/RL-LLM.html…

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Summary

This article recommends a UCLA-led online course on Reinforcement Learning for Large Language Models, covering theory, algorithms like PPO and RLHF, and practical coding exercises.

If you work in AI, take this UCLA course! Theory + practice: a deep dive into RL and LLM training from scratch. It covers MDP, PPO algorithms, the full RLHF process, and hands-on Jupyter coding. Taught by a UCLA professor with videos and assignments, ready to apply immediately after completion. Course URL: https://ernestryu.com/courses/RL-LLM.html… Stop just passively reading papers; this course will actually teach you how to train RL + LLMs. Otherwise, you won't even understand how ChatGPT was fine-tuned!
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If you’re into AI, this UCLA course is a must! It breaks down RL and LLM training from zero to one with theory + hands-on practice. Covers MDPs, PPO algorithm, RLHF workflows, and Jupyter code exercises. Taught by a UCLA professor, includes videos and assignments, and lets you get hands-on experience. Course link: https://ernestryu.com/courses/RL-LLM.html… Stop just reading papers—this course will actually teach you RL+LLM training. Otherwise, you won’t even know how ChatGPT was trained!

Reinforcement Learning of Large Language Models

Source: https://ernestryu.com/courses/RL-LLM.html

Lecture slides

  • Chapter 0: Prologue (https://ernestryu.com/courses/RL-LLM/chapter0.pdf).
  • Chapter 1: Deep Reinforcement Learning (https://ernestryu.com/courses/RL-LLM/chapter1.pdf).
  • Chapter 2: Large Language Models (https://ernestryu.com/courses/RL-LLM/chapter2.pdf).
  • Chapter 3: Reinforcement Learning of Large Language Models (https://ernestryu.com/courses/RL-LLM/chapter3.pdf).

Lecture videos

  • Chapter 0: Prologue (https://youtu.be/q9972BRoXzQ).
  • Chapter 1.1: MDP foundations, imitation learning, and value iteration (https://youtu.be/R2oT9Tcv0eU).
  • Chapter 1.2: Deep policy evaluation (https://youtu.be/KwNs7AT3UcY).
  • Chapter 1.3: Deep policy gradient methods (A3C) (https://youtu.be/iWOJpNr-kcI).
  • Chapter 1.4: Deep policy gradient methods (PPO, GRPO) (https://youtu.be/qzaX7DBloZc).
  • Chapter 1.5: AlphaGo, test-time compute, and expert iteration (https://youtu.be/8ZnVAu1tlYw).
  • Chapter 2.1: NLP foundations, language modeling, RNNs (https://youtu.be/dhu_ZYUsBnw).
  • Chapter 2.2: Transformers I (BERT, GPT-1) (https://youtu.be/q5Sl4bO-wBk).
  • Chapter 2.3: Transformers II (modern transformer updates and sampling methods) (https://youtu.be/88HtzKoSzSE).
  • Chapter 2.4: In-context learning and instruction fine-tuning (https://youtu.be/dPHrgBv4c9s).
  • Chapter 3.1: Reinforcement Learning from Human Feedback (PPO, DPO) (https://youtu.be/IijXgwZJarU).
  • Chapter 3.2: Reinforcement Learning with Verifiable Rewards (RLVR) (https://youtu.be/QKgVPTC_M1Q).

Course Information

Instructor

Ernest K. Ryu (http://www.math.snu.ac.kr/~ernestryu/) Assistant Professor of Mathematics, UCLA, Photo of Ernest Ryu

Prerequisites

Students are expected to have basic familiarity with deep learning at the level of image classification. No prior experience with reinforcement learning (RL) or large language models (LLMs) is assumed. For the deep RL lectures, students should be familiar with conditional expectations and the tower property (law of total expectation).

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