LLM-RL-Visualized: Detailed Introduction to Large Language Model and Reinforcement Learning Algorithm Learning Resources

Project Overview

LLM-RL-Visualized is an open-source learning resource library containing over 100 original diagrams illustrating Large Language Model (LLM) and Reinforcement Learning (RL) principles. It serves as a systematic visual teaching resource for LLM algorithms, covering a complete knowledge system from foundational concepts to advanced applications.

Core Content Structure

Chapter 1: LLM Principles and Technical Overview

• 1.1 Illustrated LLM Architecture
  • Panorama of Large Language Model (LLM) Architecture
  • Input Layer: Tokenization, Token Mapping, and Vector Generation
  • Output Layer: Logits, Probability Distribution, and Decoding
  • Multimodal Language Models (MLLM) and Vision-Language Models (VLM)
• 1.2 Panorama of LLM Training
• 1.3 Scaling Laws (Four Major Laws of Performance Scaling)

Chapter 2: SFT (Supervised Fine-Tuning)

• 2.1 Illustrated Various Fine-Tuning Techniques
  • Full Parameter Fine-Tuning, Partial Parameter Fine-Tuning
  • LoRA (Low-Rank Adaptation Fine-Tuning) – Achieving More with Less
  • LoRA Derivatives: QLoRA, AdaLoRA, PiSSA, etc.
  • Prompt-Based Fine-Tuning: Prefix-Tuning, Prompt Tuning, etc.
  • Adapter Tuning
  • Fine-Tuning Techniques Comparison and Selection Guide
• 2.2 In-depth Analysis of SFT Principles
  • SFT Data and ChatML Formatting
  • Logits and Token Probability Calculation
  • Illustrated SFT Labels and Loss
  • Log Probabilities (LogProbs) and LogSoftmax
• 2.3 Instruction Collection and Processing
• 2.4 SFT Practice Guide

Chapter 3: DPO (Direct Preference Optimization)

• 3.1 Core Idea of DPO
  • Implicit Reward Model
  • Loss and Optimization Objective
• 3.2 Construction of Preference Datasets
• 3.3 Illustrated DPO Implementation and Training
• 3.4 DPO Practical Experience
• 3.5 Advanced DPO

Chapter 4: Training-Free Performance Optimization Techniques

• 4.1 Prompt Engineering
• 4.2 CoT (Chain-of-Thought)
  • Illustrated CoT Principles
  • Derivatives like ToT, GoT, XoT, etc.
• 4.3 Generation Control and Decoding Strategies
  • Greedy Search, Beam Search
  • Illustrated Sampling Methods like Top-K, Top-P, etc.
• 4.4 RAG (Retrieval-Augmented Generation)
• 4.5 Function and Tool Calling

Chapter 5: Reinforcement Learning Fundamentals

• 5.1 Core of Reinforcement Learning
  • RL Basic Architecture, Core Concepts
  • Markov Decision Process (MDP)
  • Exploration vs. Exploitation, ε-Greedy Strategy
  • On-policy, Off-policy
• 5.2 Value Function, Reward Estimation
• 5.3 Temporal Difference (TD)
• 5.4 Value-Based Algorithms
• 5.5 Policy Gradient Algorithms
• 5.6 Multi-Agent Reinforcement Learning (MARL)
• 5.7 Imitation Learning (IL)
• 5.8 Advanced RL Extensions

Chapter 6: Policy Optimization Algorithms

• 6.1 Actor-Critic Architecture
• 6.2 Advantage Function and A2C
• 6.3 PPO and Related Algorithms
  • Evolution of PPO Algorithm
  • TRPO (Trust Region Policy Optimization)
  • Importance Sampling
  • Detailed Explanation of PPO-Clip
• 6.4 GRPO Algorithm
• 6.5 Deterministic Policy Gradient (DPG)

Chapter 7: RLHF and RLAIF

• 7.1 Overview of RLHF (Reinforcement Learning from Human Feedback)
  • Reinforcement Learning Modeling for Language Models
  • RLHF Training Samples, Overall Process
• 7.2 Phase One: Illustrated Reward Model Design and Training
  • Reward Model Structure
  • Reward Model Input and Reward Score
  • Analysis of Reward Model Loss
• 7.3 Phase Two: PPO Training with Multi-Model Linkage
  • Illustrated Roles of Four Models
  • KL Divergence-Based Policy Constraint
  • Core RLHF Implementation Based on PPO
• 7.4 RLHF Practical Tips
• 7.5 Reinforcement Learning from AI Feedback

Chapter 8: Logic Reasoning Capability Optimization

• 8.1 Overview of Reasoning-Related Techniques
• 8.2 Reasoning Path Search and Optimization
  • MCTS (Monte Carlo Tree Search)
  • A* Search
  • BoN Sampling and Distillation
• 8.3 Reinforcement Learning Training

Chapter 9: Integrated Practice and Performance Optimization

• 9.1 Panorama of Practice
• 9.2 Training and Deployment
• 9.3 DeepSeek Training and Local Deployment
• 9.4 Performance Evaluation
• 9.5 LLM Performance Optimization Technology Map

Resource Features

1. Visualized Teaching

• 100+ original architectural diagrams systematically explaining LLMs and Reinforcement Learning
• Richly illustrated, with meticulously designed diagrams for every complex concept
• Provides SVG vector graphics, supporting infinite zoom

2. Integration of Theory and Practice

• Not only theoretical principle diagrams but also extensive practical guides
• Provides complete code examples and pseudocode implementations
• Covers the entire process from research to engineering implementation

3. Coverage of Cutting-Edge Technologies

• Covers the latest LLM technologies: LLM, VLM, MLLM, etc.
• Includes advanced training algorithms: RLHF, DPO, GRPO, etc.
• Keeps pace with industry developments and is continuously updated

4. Systematic Learning Path

• Progressive learning from foundational concepts to advanced applications
• Chapters are organically linked, forming a complete knowledge system
• Suitable for learners of different levels

Technical Depth

Reinforcement Learning Section

• Provides a detailed introduction to the history of reinforcement learning, from its origins in the 1950s to the latest advancements with OpenAI's o1 model in 2024
• Covers core algorithms: PPO, DQN, Actor-Critic, Policy Gradient, etc.
• Specifically explains the application of reinforcement learning in large language models

LLM Fine-Tuning Techniques

• Explains in detail the core idea and implementation principles of LoRA (Low-Rank Adaptation)
• Compares and analyzes methods such as full parameter fine-tuning, LoRA, Prefix-Tuning, etc.
• Provides specific parameter settings and practical recommendations

Alignment Techniques

• Provides an in-depth analysis of RLHF's two-phase training process: Reward Model training and PPO reinforcement learning
• Details how DPO simplifies the RLHF process
• Introduces emerging alignment methods such as RLAIF, CAI, etc.

Learning Value

For Researchers

• Provides a complete theoretical framework and the latest research advancements
• Includes rich references and extended readings
• Suitable for in-depth study of various algorithm principles

For Engineers

• Offers practical implementation guides and code examples
• Includes detailed parameter settings and tuning recommendations
• Suitable for quick start and engineering deployment

For Learners

• Progressively designed learning path
• Visually rich teaching method with illustrations and text
• Covers everything from zero-basis to advanced applications

Usage Suggestions

1. Systematic Learning: Follow the chapter order to build a complete knowledge system.
2. Focused Breakthrough: Choose specific chapters for in-depth study based on your needs.
3. Practice Integration: Combine theoretical learning with code practice.
4. Stay Updated: Follow repository updates to keep up with the latest technological developments.

This learning resource provides a systematic, comprehensive, and practical knowledge platform for learners of large language models and reinforcement learning, making it one of the highest-quality Chinese learning resources in this field currently available.