llm-rl-visualized is a visual reference collection for understanding large language model and reinforcement learning algorithms.
The project addresses the challenge of grasping complex LLM and RL concepts by providing over one hundred original architecture diagrams and algorithm maps. It covers the core principles of large language models and vision-language models, training methodologies including reinforcement learning, RLHF, GRPO, DPO, and SFT, as well as optimization techniques and retrieval-augmented generation. The diagrams are provided as both image files and scalable vector graphics that can be zoomed infinitely and searched for text.
This collection suits developers and researchers seeking to understand the theoretical foundations and practical implementations of modern language models and reinforcement learning approaches. It works best as a reference resource alongside deeper study, with the README indicating that more detailed explanations are available in an associated book. The material spans foundational concepts like transformer structures and decoding processes through advanced topics like direct preference optimization and chain-of-thought reasoning.
The project maintains an active approach to accuracy and completeness, with the README explicitly noting that the repository undergoes ongoing corrections and additions. The collection is organized into five major sections progressing from high-level architecture overviews through specific techniques, suggesting a structured effort to present material in a logical learning sequence.
