Design Patterns & Techniques

🔗

Prompt Chaining

🔀

Routing

⚡

Parallelization

🪞

Reflection

🔧

Tool Use

🎯

Planning

👥

Multi-Agent

🧠

Memory Management

📈

Learning and Adaptation

🏗️

Fault Tolerance Infrastructure

📚

Knowledge Retrieval (RAG)

🧠

Reasoning Techniques

🔐

Security & Privacy Patterns

📊

Evaluation and Monitoring

🧠

Context Management

🎨

UI/UX & Human-AI Interaction

Loading...

🎯

Reinforcement Learning from Human Feedback(RLHF)

Training AI agents to align with human preferences through reinforcement learning on human feedback

Complexity: highLearning and Adaptation

🎯 30-Second Overview

Pattern: Three-phase training: SFT → Reward Model → PPO optimization with human preference alignment

Why: Aligns AI behavior with human values, improves safety, helpfulness, and reduces harmful outputs

Key Insight: Human preferences > reward model > policy optimization creates scalable alignment mechanism

⚡ Quick Implementation

1SFT:Supervised fine-tuning on demonstrations

2Collect:Human preference comparisons

3Train RM:Reward model on preferences

4PPO:Policy optimization with RM rewards

5Evaluate:Human preference win rates

Example: base_model → sft_model → preference_data → reward_model → aligned_model

📋 Do's & Don'ts

✅Use diverse, high-quality human preference data

✅Apply KL regularization to prevent policy drift

✅Monitor reward hacking and Goodhart's law effects

✅Use multiple evaluation metrics beyond reward scores

✅Implement careful hyperparameter tuning for PPO

✅Validate reward model correlation with human judgments

❌Skip reward model validation on held-out data

❌Use biased or low-quality preference annotations

❌Ignore distribution shift in deployment

❌Optimize purely for reward model scores

❌Use unstable RL training without safety measures

🚦 When to Use

Use When

• Human preference alignment crucial
• Safety and helpfulness requirements
• Complex subjective quality judgments
• Large-scale deployment with user interaction
• Need for controllable AI behavior

Avoid When

• Simple objective tasks with clear metrics
• Limited human annotation budget
• Real-time inference requirements
• Tasks with well-defined ground truth
• Small-scale or research-only applications

📊 Key Metrics

Human Preference Win Rate

% preferred over baseline

Reward Model Accuracy

Agreement with human labels

KL Divergence

Policy drift from reference model

Helpfulness Score

Task completion quality

Harmlessness Rate

% safe responses

PPO Training Stability

Reward curve convergence

💡 Top Use Cases

Conversational AI: Align chatbots for helpful, harmless, honest responses

Content Generation: Optimize creative writing for human preferences

Code Assistants: Improve code quality and safety recommendations

Summarization: Generate summaries matching human quality judgments

Question Answering: Provide accurate, well-formatted answers

Creative Tools: Align AI art/music generation with aesthetic preferences

Reinforcement Learning from Human Feedback

Interactive RLHF training pipeline with human preferences

RLHF Pipeline

Generation

Feedback

Training

Evaluation

Model Version: v1 • Training Steps: 0

Current Example

Waiting for generation...

Policy Metrics

Average Reward0.250

KL Divergence0.0150

Human Alignment45.0%

Response Quality52.0%

Reward Signals

ppo

-0.200

reward_model

+0.700

human

+0.300

Human Reward Model PPO

Training History

No training examples yet

RLHF Algorithm Overview

Core Process: Generate multiple responses → Collect human preferences → Train reward model → Optimize policy with PPO while maintaining KL constraint.

Key Components: Supervised fine-tuning (SFT), reward modeling from preferences, proximal policy optimization (PPO) with KL penalty.

Human Feedback: Pairwise comparisons, rankings, or direct ratings that teach the model human values and preferences.

Benefits: Aligns model behavior with human values, improves helpfulness and safety, reduces harmful outputs.

References & Further Reading

Deepen your understanding with these curated resources

Foundational Papers

Training language models to follow instructions with human feedback (Ouyang et al., 2022)

Learning to summarize from human feedback (Stiennon et al., 2020)

Constitutional AI: Harmlessness from AI Feedback (Bai et al., 2022)

Proximal Policy Optimization Algorithms (Schulman et al., 2017)

Recent Advances (2023-2024)

RLHF: Scaling Reinforcement Learning from Human Feedback with AI Feedback (Bai et al., 2023)

Direct Preference Optimization: Your Language Model is Secretly a Reward Model (Rafailov et al., 2023)

Statistical Rejection Sampling Improves Preference Optimization (Liu et al., 2024)

Secrets of RLHF in Large Language Models (Zheng et al., 2024)

Implementation Resources

Hugging Face TRL Library - RLHF Implementation

OpenAI RLHF Training Guide

Anthropic Constitutional AI Implementation

DeepSpeed Chat - RLHF Training Framework

Industry Applications

ChatGPT Technical Report (OpenAI, 2023)

LLaMA 2: Open Foundation and Fine-Tuned Chat Models (Touvron et al., 2023)

Claude 2 Technical Report (Anthropic, 2023)

Sparrow: Training helpful and harmless assistants (DeepMind, 2022)

Evaluation & Safety

Red Teaming Language Models to Reduce Harms (Ganguli et al., 2022)

Constitutional AI Evaluation Methods (Anthropic, 2023)

Measuring Progress on Scalable Oversight for Large Language Models (OpenAI, 2022)

AI Alignment Forum - RLHF Discussion

Datasets & Benchmarks

Anthropic HH-RLHF Dataset

OpenAI Human Preference Dataset

Stanford Human Preferences Dataset (SHP)

UltraFeedback: Large-scale Preference Dataset

Tools & Frameworks

Transformers Reinforcement Learning (TRL)

OpenRL: Distributed Reinforcement Learning Framework

RLHF Implementation in JAX

RLHF Training with DeepSpeed

Community & Discussion

RLHF Community Discord

Alignment Research Center

ML Safety Newsletter - RLHF Updates

EleutherAI RLHF Research Group

Contribute to this collection

Know a great resource? Submit a pull request to add it.

Contribute

🎯

Reinforcement Learning from Human Feedback(RLHF)

Training AI agents to align with human preferences through reinforcement learning on human feedback

Complexity: highLearning and Adaptation

🎯 30-Second Overview

Pattern: Three-phase training: SFT → Reward Model → PPO optimization with human preference alignment

Why: Aligns AI behavior with human values, improves safety, helpfulness, and reduces harmful outputs

Key Insight: Human preferences > reward model > policy optimization creates scalable alignment mechanism

⚡ Quick Implementation

1SFT:Supervised fine-tuning on demonstrations

2Collect:Human preference comparisons

3Train RM:Reward model on preferences

4PPO:Policy optimization with RM rewards

5Evaluate:Human preference win rates

Example: base_model → sft_model → preference_data → reward_model → aligned_model

📋 Do's & Don'ts

✅Use diverse, high-quality human preference data

✅Apply KL regularization to prevent policy drift

✅Monitor reward hacking and Goodhart's law effects

✅Use multiple evaluation metrics beyond reward scores

✅Implement careful hyperparameter tuning for PPO

✅Validate reward model correlation with human judgments

❌Skip reward model validation on held-out data

❌Use biased or low-quality preference annotations

❌Ignore distribution shift in deployment

❌Optimize purely for reward model scores

❌Use unstable RL training without safety measures

🚦 When to Use

Use When

• Human preference alignment crucial
• Safety and helpfulness requirements
• Complex subjective quality judgments
• Large-scale deployment with user interaction
• Need for controllable AI behavior

Avoid When

• Simple objective tasks with clear metrics
• Limited human annotation budget
• Real-time inference requirements
• Tasks with well-defined ground truth
• Small-scale or research-only applications

📊 Key Metrics

Human Preference Win Rate

% preferred over baseline

Reward Model Accuracy

Agreement with human labels

KL Divergence

Policy drift from reference model

Helpfulness Score

Task completion quality

Harmlessness Rate

% safe responses

PPO Training Stability

Reward curve convergence

💡 Top Use Cases

Conversational AI: Align chatbots for helpful, harmless, honest responses

Content Generation: Optimize creative writing for human preferences

Code Assistants: Improve code quality and safety recommendations

Summarization: Generate summaries matching human quality judgments

Question Answering: Provide accurate, well-formatted answers

Creative Tools: Align AI art/music generation with aesthetic preferences

Reinforcement Learning from Human Feedback

Interactive RLHF training pipeline with human preferences

RLHF Pipeline

Generation

Feedback

Training

Evaluation

Model Version: v1 • Training Steps: 0

Current Example

Waiting for generation...

Policy Metrics

Average Reward0.250

KL Divergence0.0150

Human Alignment45.0%

Response Quality52.0%

Reward Signals

ppo

-0.200

reward_model

+0.700

human

+0.300

Human Reward Model PPO

Training History

No training examples yet

RLHF Algorithm Overview

Core Process: Generate multiple responses → Collect human preferences → Train reward model → Optimize policy with PPO while maintaining KL constraint.

Key Components: Supervised fine-tuning (SFT), reward modeling from preferences, proximal policy optimization (PPO) with KL penalty.

Human Feedback: Pairwise comparisons, rankings, or direct ratings that teach the model human values and preferences.

Benefits: Aligns model behavior with human values, improves helpfulness and safety, reduces harmful outputs.

References & Further Reading

Deepen your understanding with these curated resources

Foundational Papers

Training language models to follow instructions with human feedback (Ouyang et al., 2022)

Learning to summarize from human feedback (Stiennon et al., 2020)

Constitutional AI: Harmlessness from AI Feedback (Bai et al., 2022)

Proximal Policy Optimization Algorithms (Schulman et al., 2017)

Recent Advances (2023-2024)

RLHF: Scaling Reinforcement Learning from Human Feedback with AI Feedback (Bai et al., 2023)

Direct Preference Optimization: Your Language Model is Secretly a Reward Model (Rafailov et al., 2023)

Statistical Rejection Sampling Improves Preference Optimization (Liu et al., 2024)

Secrets of RLHF in Large Language Models (Zheng et al., 2024)

Implementation Resources

Hugging Face TRL Library - RLHF Implementation

OpenAI RLHF Training Guide

Anthropic Constitutional AI Implementation

DeepSpeed Chat - RLHF Training Framework

Industry Applications

ChatGPT Technical Report (OpenAI, 2023)

LLaMA 2: Open Foundation and Fine-Tuned Chat Models (Touvron et al., 2023)

Claude 2 Technical Report (Anthropic, 2023)

Sparrow: Training helpful and harmless assistants (DeepMind, 2022)

Evaluation & Safety

Red Teaming Language Models to Reduce Harms (Ganguli et al., 2022)

Constitutional AI Evaluation Methods (Anthropic, 2023)

Measuring Progress on Scalable Oversight for Large Language Models (OpenAI, 2022)

AI Alignment Forum - RLHF Discussion

Datasets & Benchmarks

Anthropic HH-RLHF Dataset

OpenAI Human Preference Dataset

Stanford Human Preferences Dataset (SHP)

UltraFeedback: Large-scale Preference Dataset

Tools & Frameworks

Transformers Reinforcement Learning (TRL)

OpenRL: Distributed Reinforcement Learning Framework

RLHF Implementation in JAX

RLHF Training with DeepSpeed

Community & Discussion

RLHF Community Discord

Alignment Research Center

ML Safety Newsletter - RLHF Updates

EleutherAI RLHF Research Group

Contribute to this collection

Know a great resource? Submit a pull request to add it.

Contribute

Patterns

closed

Design Patterns & Techniques

🔗

Prompt Chaining

🔀

Routing

⚡

Parallelization

🪞

Reflection

🔧

Tool Use

🎯

Planning

👥

Multi-Agent

🧠

Memory Management

📈

Learning and Adaptation

🏗️

Fault Tolerance Infrastructure

📚

Knowledge Retrieval (RAG)

🧠

Reasoning Techniques

🔐

Security & Privacy Patterns

📊

Evaluation and Monitoring

🧠

Context Management

🎨

Agentic Design

Agentic Design

Design Patterns & Techniques

Prompt Chaining

Routing

Parallelization

Reflection

Tool Use

Planning

Multi-Agent

Memory Management

Learning and Adaptation

Reinforcement Learning from Human Feedback(RLHF)

Direct Preference Optimization(DPO)

In-Context Learning(ICL)

Meta-Learning Systems(MLS)

Continual Learning(CL)

Self-Improving Systems(SIS)

Constitutional AI(CAI)

Reinforcement Learning from AI Feedback(RLAIF)

Test-Time Scaling(TTS)

Odds Ratio Preference Optimization(ORPO)

Simple Preference Optimization(SimPO)

Supervised Learning for Agents(SLA)

Unsupervised Learning for Agents(ULA)

Online Learning for Agents(OLA)

Fault Tolerance Infrastructure

Knowledge Retrieval (RAG)

Reasoning Techniques

Security & Privacy Patterns

Evaluation and Monitoring

Context Management

UI/UX & Human-AI Interaction

Loading...

Reinforcement Learning from Human Feedback(RLHF)

🎯 30-Second Overview

⚡ Quick Implementation

📋 Do's & Don'ts

🚦 When to Use

Use When

Avoid When

📊 Key Metrics

💡 Top Use Cases

Reinforcement Learning from Human Feedback

RLHF Pipeline

Current Example

Policy Metrics

Reward Signals

Training History

RLHF Algorithm Overview

References & Further Reading

Foundational Papers

Recent Advances (2023-2024)

Implementation Resources

Industry Applications

Evaluation & Safety

Datasets & Benchmarks

Tools & Frameworks

Community & Discussion

Contribute to this collection

Reinforcement Learning from Human Feedback(RLHF)

🎯 30-Second Overview

⚡ Quick Implementation

📋 Do's & Don'ts

🚦 When to Use

Use When

Avoid When

📊 Key Metrics

💡 Top Use Cases

Reinforcement Learning from Human Feedback

RLHF Pipeline

Current Example

Policy Metrics

Reward Signals

Training History

RLHF Algorithm Overview

References & Further Reading

Foundational Papers

Recent Advances (2023-2024)

Implementation Resources