@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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@wsl8297: UC's Open Course on Reinforcement Learning for LLMs uses a 'theory + practice' approach to thoroughly explain key AI training techniques from the ground up, helping you systematically build a complete framework spanning from RL to LLM training. Comprehensive curriculum paired with complete resources: lecture slides, full videos, and practical exercises are all provided so you can start implementing right away…

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Assistant Professor Ernest K. Ryu at UCLA offers the open course 'Reinforcement Learning for Large Language Models,' comprehensively analyzing key LLM training techniques like RLHF, PPO, and DPO alongside their supporting resources through a blend of theory and practice. The course provides developers and researchers with a systematic learning path from foundational algorithms to practical deployment.