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LLM-RL-Visualized

changyeyu/LLM-RL-Visualized

Provides over 100 diagrams illustrating LLM and RL algorithms

GraphCanon updated Sep 20, 2026 · GitHub synced Sep 20, 2026

31views this month

4.9k stars468 forksLast push Aug 29, 2026 Python Other

Decision brief

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

Good fit when

  • When detailed visual explanations of LLM and RL algorithms are needed
  • For studying fine-tuning techniques like DPO with specific visual aids

Avoid when

  • 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

Observed Jul 15, 2026 · Source: enrich:decision_facts

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Maintenance and security

Full trust report
Maintenance
Active (9d since push)
As of Sep 7, 2026
Provenance
Not a fork · Personal account
As of Sep 7, 2026
Security (OSV)
No lockfile
As of Jul 11, 2026

Public GitHub metadata and optional OSV scans. Signals, not a guarantee. Trust methodology.

Install

pip install LLM-RL-Visualized
PyPI

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Evidence and technical details

Sourced facts, taxonomy, compatibility claims, README excerpt, and machine-readable endpoints.

Overview

A repository containing diagrams that explain the principles of Large Language Models (LLM), Vision-Language Models (VLM), reinforcement learning (RL) algorithms, and training methods. It offers insights into SFT fine-tuning techniques, DPO direct preference optimization, and optimization technologies.

Capability facts

Languages
python

Source: github.language · Sep 7, 2026

Categories

Tags

README

<p align="center" <img src="src/assets/banner/幻灯片2.SVG" alt="图解大模型算法:LLM / RL / VLM 核心技术图谱" / </p <p align="center" <a href="./src/README EN.md" <img alt="English Version" src="https://img.shields.io/badge/English Version blue?style=for the badge" height="45" / </a &nbsp;&nbsp; <...

For agents

This page has a .md twin and JSON over the API.

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