---
title: "LLM-RL-Visualized vs LLMs-from-scratch"
type: "comparison"
canonical_url: "https://www.graphcanon.com/compare/changyeyu-llm-rl-visualized-vs-rasbt-llms-from-scratch"
tools: ["changyeyu-llm-rl-visualized", "rasbt-llms-from-scratch"]
---

# LLM-RL-Visualized vs LLMs-from-scratch

*GraphCanon updated Aug 16, 2026*

## Verdict

Pick LLM-RL-Visualized if lLM-RL-Visualized offers over 100 diagrams for understanding LLM, RL algorithms, and training methods including SFT, DPO and optimization techniques; pick LLMs-from-scratch if lLMs-from-scratch is a project-oriented repository aimed at building PyTorch-based language models from the ground up, with detailed step-by-step instructions.

[LLM-RL-Visualized](https://book.douban.com/subject/37331056/) reports 4.8k GitHub stars, 455 forks, and 3 open issues, last pushed Jul 27, 2026. [LLMs-from-scratch](https://amzn.to/4fqvn0D) has 103k stars, 16k forks, and 2 open issues, last pushed Aug 10, 2026. Figures are from public GitHub metadata via [LLM-RL-Visualized's repository](https://github.com/changyeyu/LLM-RL-Visualized) and [LLMs-from-scratch's repository](https://github.com/rasbt/LLMs-from-scratch).

| | [LLM-RL-Visualized](/tools/changyeyu-llm-rl-visualized.md) | [LLMs-from-scratch](/tools/rasbt-llms-from-scratch.md) |
| --- | --- | --- |
| Tagline | Provides over 100 diagrams illustrating LLM and RL algorithms | Implement a ChatGPT-like LLM in PyTorch from scratch, step by step |
| Stars | 4,750 | 102,733 |
| Forks | 455 | 15,748 |
| Open issues | 3 | 2 |
| Language | Python | Jupyter Notebook |
| Adopt for | LLM-RL-Visualized offers over 100 diagrams for understanding LLM, RL algorithms, and training methods including SFT, DPO and optimization techniques. | LLMs-from-scratch is a project-oriented repository aimed at building PyTorch-based language models from the ground up, with detailed step-by-step instructions. |
| Persona | - | - |
| Runtime | - | - |
| License | Other | Other |
| Categories | LLM Frameworks, Model Training | LLM Frameworks, Model Training |

## Trust and health

_Sourced signals - not a safety guarantee. No winner column._

| | [LLM-RL-Visualized](/tools/changyeyu-llm-rl-visualized.md) | [LLMs-from-scratch](/tools/rasbt-llms-from-scratch.md) |
| --- | --- | --- |
| Maintenance | Active (82%) | Very active (96%) |
| Days since push | 11d | 5d |
| Open issues (now) | 3 | 2 |
| Stars delta | Unknown | +3.5k (30d) |
| Open issues delta | Unknown | -1 (30d) |
| Full report | [trust report](/tools/changyeyu-llm-rl-visualized/trust.md) | [trust report](/tools/rasbt-llms-from-scratch/trust.md) |

## Decision facts: LLM-RL-Visualized

- **Adopt for:** LLM-RL-Visualized offers over 100 diagrams for understanding LLM, RL algorithms, and training methods including SFT, DPO and optimization techniques.

## Decision facts: LLMs-from-scratch

- **Adopt for:** LLMs-from-scratch is a project-oriented repository aimed at building PyTorch-based language models from the ground up, with detailed step-by-step instructions.

## Choose when

### Choose LLM-RL-Visualized if…

- LLM-RL-Visualized is primarily Python; LLMs-from-scratch is Jupyter Notebook.
- Tags unique to LLM-RL-Visualized: algorithm, llm, machine-learning, natural-language-processing.
- When detailed visual explanations of LLM and RL algorithms are needed

### Choose LLMs-from-scratch if…

- LLMs-from-scratch is primarily Jupyter Notebook; LLM-RL-Visualized is Python.
- Tags unique to LLMs-from-scratch: artificial-intelligence, attention-mechanism, finetuning, from-scratch.
- - You are an advanced practitioner aiming to fully understand the underpinnings of LLMs using PyTorch as your primary framework.

## When NOT to use LLM-RL-Visualized

- If looking for executable code or tools rather than diagrams and visual explanations alone
- For datasets or large-scale experimental setups that require more interactive coding environments

## When NOT to use LLMs-from-scratch

- - If you are looking for a rapid deployment of an LLM without understanding its intricate structure - this tool requires extensive manual and conceptual work.
- - You prefer frameworks with automatic model generation or other high-level abstractions that simplify the process. This repository emphasizes manual creation, which is more time-consuming but offers
- a deeper learning experience.

## Common questions

### What is the difference between LLM-RL-Visualized and LLMs-from-scratch?

LLM-RL-Visualized: Provides over 100 diagrams illustrating LLM and RL algorithms. LLMs-from-scratch: Implement a ChatGPT-like LLM in PyTorch from scratch, step by step. See the comparison table for live GitHub stats and shared categories.

### When should I choose LLM-RL-Visualized over LLMs-from-scratch?

Choose LLM-RL-Visualized over LLMs-from-scratch when LLM-RL-Visualized is primarily Python; LLMs-from-scratch is Jupyter Notebook; Tags unique to LLM-RL-Visualized: algorithm, llm, machine-learning, natural-language-processing; When detailed visual explanations of LLM and RL algorithms are needed.

### When should I choose LLMs-from-scratch over LLM-RL-Visualized?

Choose LLMs-from-scratch over LLM-RL-Visualized when LLMs-from-scratch is primarily Jupyter Notebook; LLM-RL-Visualized is Python; Tags unique to LLMs-from-scratch: artificial-intelligence, attention-mechanism, finetuning, from-scratch; - You are an advanced practitioner aiming to fully understand the underpinnings of LLMs using PyTorch as your primary framework.

### When should I avoid LLM-RL-Visualized?

If looking for executable code or tools rather than diagrams and visual explanations alone For datasets or large-scale experimental setups that require more interactive coding environments

### When should I avoid LLMs-from-scratch?

- If you are looking for a rapid deployment of an LLM without understanding its intricate structure - this tool requires extensive manual and conceptual work. - You prefer frameworks with automatic model generation or other high-level abstractions that simplify the process. This repository emphasizes manual creation, which is more time-consuming but offers a deeper learning experience.

### Is LLM-RL-Visualized or LLMs-from-scratch more popular on GitHub?

LLMs-from-scratch has more GitHub stars (102,733 vs 4,750). Stars measure visibility, not whether either tool fits your constraints.

### Are LLM-RL-Visualized and LLMs-from-scratch open source?

Yes - both are open-source projects on GitHub (LLM-RL-Visualized: Other, LLMs-from-scratch: Other).

### Where can I find alternatives to LLM-RL-Visualized or LLMs-from-scratch?

GraphCanon lists graph-backed alternatives at [LLM-RL-Visualized alternatives](/tools/changyeyu-llm-rl-visualized/alternatives) and [LLMs-from-scratch alternatives](/tools/rasbt-llms-from-scratch/alternatives) ([LLM-RL-Visualized markdown twin](/tools/changyeyu-llm-rl-visualized/alternatives.md), [LLMs-from-scratch markdown twin](/tools/rasbt-llms-from-scratch/alternatives.md)), ranked by typed relationship edges rather than popularity votes.

### Is there a machine-readable version of this comparison?

Yes. The markdown twin at [this comparison](/compare/changyeyu-llm-rl-visualized-vs-rasbt-llms-from-scratch.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers.

### Which is better maintained, LLM-RL-Visualized or LLMs-from-scratch?

LLM-RL-Visualized: Active. LLMs-from-scratch: Very active. Compare maintenance labels, days since push, and release cadence in the trust section below - stars alone do not measure maintenance.

### Where are the full trust reports for LLM-RL-Visualized and LLMs-from-scratch?

GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [LLM-RL-Visualized trust report](/tools/changyeyu-llm-rl-visualized/trust); [LLMs-from-scratch trust report](/tools/rasbt-llms-from-scratch/trust).

---

**Machine-readable endpoints**

- JSON: [`/api/graphcanon/graph?tool=changyeyu-llm-rl-visualized`](/api/graphcanon/graph?tool=changyeyu-llm-rl-visualized)
- LLM index: [/llms.txt](/llms.txt)
- Full corpus: [/llms-full.txt](/llms-full.txt)

_GraphCanon - The knowledge graph for AI development. https://www.graphcanon.com/_