Lesson 101: Reinforcement Learning from Human Feedback (RLHF)

After fine-tuning, a language model becomes good at following instructions. However, it still does not fully understand what humans prefer. This is where Reinforcement Learning from Human Feedback, commonly called RLHF, plays a critical role.

RLHF teaches a model how to behave better by learning directly from human judgments instead of fixed rules or labels.

What Is RLHF?

Reinforcement Learning from Human Feedback is a training technique where humans guide a model by ranking or scoring its outputs. The model then learns to produce responses that humans prefer.

• The model generates multiple answers
• Humans compare and rank those answers
• The model learns from these preferences

Instead of learning what is “correct”, the model learns what is “better”.

Real-World Analogy

Imagine training a customer support agent. You do not tell them exact sentences to say every time. Instead, you review their responses and say which ones are better.

Over time, the agent naturally learns the preferred tone, clarity, and helpfulness. RLHF works the same way for AI systems.

Why Fine-Tuning Alone Is Not Enough

Supervised fine-tuning teaches a model to imitate examples. But imitation alone has limits.

• Some responses are technically correct but unhelpful
• Some answers sound rude or unsafe
• Some replies ignore user intent

RLHF helps models learn subtle human preferences that are hard to write as rules.

How RLHF Works (High-Level Flow)

RLHF is usually performed in three main stages.

• Train a base language model
• Train a reward model using human feedback
• Optimize the language model using reinforcement learning

Step 1: Collect Human Feedback

The model is asked to generate multiple responses for the same prompt. Humans then rank these responses from best to worst.

prompt = "Explain AI to a beginner"

response_1 = model.generate(prompt)
response_2 = model.generate(prompt)

human_rank = rank(response_1, response_2)
  

Here, humans decide which response is clearer, safer, or more helpful.

Step 2: Train the Reward Model

The reward model learns to predict which responses humans prefer.

reward = reward_model(prompt, response)

loss = compare(reward, human_preference)
update(reward_model, loss)
  

This reward model becomes a stand-in for human judgment during training.

Step 3: Reinforcement Learning Optimization

The language model is optimized to maximize the reward predicted by the reward model.

for step in training_steps:
    response = model.generate(prompt)
    reward = reward_model(prompt, response)
    update_model_using_rl(model, reward)
  

This step teaches the model to consistently produce responses that humans like.

Why RLHF Is So Important

RLHF is the reason modern AI systems feel:

• More helpful
• More polite
• More aligned with human values

Without RLHF, models would often generate correct but unsafe or confusing outputs.

Challenges of RLHF

Although powerful, RLHF is not perfect.

• Human feedback is expensive to collect
• Different humans may disagree
• Bias can be introduced if feedback is inconsistent

Careful design and diverse feedback are necessary for reliable alignment.

Practice Questions

Quick Quiz

Coming up next: Advanced Prompt Engineering — how prompts shape reasoning, behavior, and output quality in LLMs.