LLM-RL-Visualized: 100+ Architecture Diagrams That Actually Explain How Modern LLMs Work

Nearly 4,000 stars in under a year, with commits still landing in early 2026 — this repo has been quietly accumulating a fanbase among people who learn better from diagrams than from dense math papers. I spent a few hours going through it, and I want to give you an honest take on whether it's worth your time.

What This Repo Actually Is

This is not a code library. There's no pip install, no API, no framework to integrate. What changyeyu has built here is a comprehensive visual reference — over 100 original SVG and PNG architecture diagrams covering the full stack of modern LLM training: transformer internals, SFT, DPO, PPO, GRPO, RLHF, RLAIF, RAG, reasoning chains, quantization, and a lot more.

The diagrams are companion material to the author's book 《大模型算法：强化学习、微调与对齐》 (roughly: "Large Model Algorithms: Reinforcement Learning, Fine-Tuning and Alignment"), but the repo stands on its own. You don't need the book to get value here.

The primary language in the diagrams is Chinese, but there's an English README and many of the diagrams are either bilingual or use enough standard notation that a non-Chinese reader can follow along. It's not a dealbreaker, but it's worth knowing upfront.

Why This Matters Right Now

Here's the problem this repo is solving: the gap between "I read the Attention Is All You Need paper" and "I understand how DeepSeek actually trains its reasoning model" is enormous. That gap is full of blog posts that are either too shallow or papers that assume you already know everything.

Visual explanations of RL concepts applied to LLMs are genuinely hard to find at this level of detail. Most resources either treat RL and LLMs as separate domains, or they give you a high-level cartoon that glosses over the parts that actually matter — like how the four models in PPO-based RLHF interact, or what the KL divergence calculation looks like in practice during PPO training.

This repo sits in a sweet spot: detailed enough to be technically useful, visual enough to build intuition quickly. That's a rare combination.

What I Found Genuinely Useful

The PPO/GRPO section is the standout. The diagrams walking through how PPO works in the RLHF context — the four-model setup (policy, reference, reward, value), how they're initialized, how gradients flow — are among the clearest I've seen anywhere. The comparison between PPO and GRPO is particularly well done given how much confusion exists around GRPO since the DeepSeek-R1 paper dropped.

The RL fundamentals coverage is surprisingly thorough. This isn't just "here's a transformer diagram with PPO bolted on." There are dedicated diagrams for Monte Carlo vs TD methods, DQN, Actor-Critic, GAE, importance sampling, and more. If you're a pure ML engineer who's been avoiding RL because it felt like a separate discipline, this is a reasonable on-ramp.

The SVG format is a real practical win. The diagrams are available as scalable vector files, which means you can zoom in infinitely without losing quality, and in many cases you can actually select and copy the text. For reference material you're going to return to repeatedly, this matters more than it sounds.

The LLM structure overview diagram — what the author calls the largest LLM structure diagram on the Chinese internet — is genuinely impressive in scope. It covers Decoder-Only and MoE architectures in a single view, with enough detail to trace the data flow through attention, FFN, normalization, and output layers.

Active maintenance. Commits are still coming in. Gemma 4 was added in early April 2026. GLM-5 before that. The LLM/VLM index file gets updated regularly. This isn't an abandoned project.

Who Should Use This

You'll get the most out of this if you're: - An ML engineer who works with LLMs professionally and wants to build cleaner mental models of training algorithms - Someone preparing to read papers like the PPO paper, DeepSeek-R1, or InstructGPT and wants visual scaffolding before diving in - A researcher or technical writer who needs reference diagrams (check the license situation first — more on that below) - Someone who's comfortable with the math but struggles to hold the full system architecture in their head simultaneously

This is probably not for you if: - You're a complete beginner to deep learning. The diagrams assume you know what attention is, what a gradient is, and roughly how neural networks train. There's no hand-holding at that level. - You need English-first content. The diagrams are primarily in Chinese. The English README helps, but the actual diagram labels are mostly Chinese. If you can't read any Chinese, you'll still get value from the structural layout, but you'll miss nuance. - You're looking for runnable code. There is essentially no executable code here. The Python language tag on GitHub is misleading — I didn't find meaningful Python files. This is a documentation/reference repo.

Concerns and Limitations

The license situation is unclear. GitHub shows "NOASSERTION" for the license, which typically means there's a license file that GitHub couldn't parse, or it's custom. The README mentions the diagrams are original works tied to a published book. Before you use these diagrams in presentations, papers, or your own educational content, you need to clarify the usage rights. I wouldn't assume these are freely reusable without attribution at minimum.

It's a single contributor project. 97 of the commits are from changyeyu. That's not inherently a problem — the author is clearly active and knowledgeable — but it means the project's longevity is tied to one person's continued interest. There's no community of contributors maintaining this if the author moves on.

The depth is uneven. The PPO/RLHF sections are excellent. Some of the other sections feel more like overview slides than deep explanations. The RAG diagram, for example, is useful but not particularly novel — you've probably seen similar diagrams elsewhere. The value is concentrated in the RL-for-LLMs content.

No versioning or releases. There are no tagged releases. If you reference a specific diagram in a document or course, the URL could change or the diagram could be updated without notice. For personal reference this is fine; for anything more formal it's a consideration.

The index file is a bit unwieldy. The LLM-VLM-index summary file that gets updated frequently is a long flat list of papers, models, and links. It's potentially useful as a reading list, but it's not well-organized enough to be a reliable reference index. It reads more like a running bookmark collection than a curated resource.

Verdict

This is a genuinely useful reference repository that I'll keep bookmarked. The PPO/GRPO/RLHF visual explanations alone are worth the visit — I've read multiple papers and blog posts on these topics and still found diagrams here that clarified things I'd been hand-waving through.

The caveats are real: it's Chinese-first, there's no code, the license is ambiguous, and the quality varies by section. But as a visual reference for understanding how modern LLM training algorithms actually work at a systems level, it fills a gap that I haven't found filled as well anywhere else in English, let alone Chinese.

If you're working in the LLM training space and you're not fluent in the full RL stack — which honestly describes most applied ML engineers right now — spend 30 minutes clicking through the diagrams in sections 6, 7, and 8. You'll probably learn something.

Just don't go in expecting a software library. It's a textbook in diagram form, and a pretty good one.

Repo: https://github.com/changyeyu/LLM-RL-Visualized