OpenAI o1 PPO vs. DeepSeek R1 GRPO: A Beginner-Friendly & Technical Breakdown

Training AI models is slow and expensive. Traditional Proximal Policy Optimization (PPO)—used in OpenAI’s o1 model—requires training two models:

1. A policy model (the AI making decisions).
2. A critic model (which judges those decisions and assigns rewards).

This means more computational power, longer training times, and higher costs.

Enter Group Relative Policy Optimization (GRPO)—the method used by DeepSeek R1. GRPO removes the critic model entirely and instead ranks multiple responses at once, making AI training faster, cheaper, and just as effective with DeepSeek integrations.

Before we dive into the technical breakdown, let’s first explain GRPO using something we all understand: pizza!

Beginner-Friendly Explanation of GRPO (The Pizza Analogy)

Imagine You’re in a Pizza Competition

You and four friends are in a contest to see who makes the best pizza. A judge comes in to decide whose pizza is the tastiest.

• The best pizza gets - 5/5 rating
• The second-best gets - 4/5 rating
• The third-best gets - 3/5 rating
• The rest get - 1/5 rating

Each round, you learn from the best pizza and try to improve your recipe.

The Old Way: One-by-One Judging (PPO)

Instead of ranking all pizzas at once, the judge compares only two pizzas at a time and keeps repeating:

"Pizza A is better than Pizza B."

"Pizza C is better than Pizza D."

"Pizza B is better than Pizza D."

The judge takes FOREVER because they have to compare each pizza one by one instead of ranking them all at once.

This is how AI used to learn using PPO (Proximal Policy Optimization).

• Slow
• Needs extra judges (extra models in AI training)
• Wastes time and effort

The New Way: Smart Judging (GRPO)

Instead of doing *one-by-one* comparisons, the judge looks at ALL pizzas at once and ranks them from best to worst in one go!

No extra steps.

No extra helpers.

Just a quick, smart decision on which pizza is best.

TThis is how GRPO (Group Relative Policy Optimization) works.

• Faster learning
• No extra judge needed (fewer AI models = lower cost)
• Smarter ranking = better decisions

Why GRPO is the Future of AI?

AI models are like pizza chefs—they need to learn fast and get better quickly.

PPO makes them learn too slowly, while GRPO helps them learn smarter, faster, and cheaper.

That’s why companies like DeepSeek AI use GRPO instead of PPO—to train AI models more efficiently without wasting resources.

Technical Breakdown: PPO vs. GRPO

Now that we’ve explained GRPO in simple terms, let’s compare it to PPO in a technical deep dive.

How PPO Works (OpenAI o1 Policy)

Proximal Policy Optimization (PPO) is the traditional way AI models learn from human feedback (RLHF). It requires:

1. A policy model that generates responses.
2. A critic model that evaluates those responses and assigns a reward.

The critic model continuously assigns scores to each response, helping the policy model improve. But this means two models are trained at once, doubling the computational cost.

How GRPO Works (DeepSeek R1 Policy)

Group Relative Policy Optimization (GRPO)* simplifies AI training:

• No separate critic model
• Compares multiple responses in a batch
• Uses ranking instead of individual scoring

Instead of evaluating responses one by one, GRPO ranks all possible responses at once. This removes redundant calculations, making training faster and more cost-effective.

Side-by-Side Comparison: PPO vs. GRPO

GRPO is simply more efficient than PPO.

Case Study: How DeepSeek R1 Used GRPO for Self-Verification & Search

DeepSeek R1 is an AI model that was trained with GRPO instead of PPO. This allowed it to:

1. Compare multiple answers at once, reinforcing the best response
2. Follow structured reasoning, improving decision-making
3. Improve factual accuracy, making AI verify its own responses before answering

Example: How DeepSeek R1 Self-Verifies Its Answers

DeepSeek R1 structures its reasoning step-by-step before answering:

xml
<reasoning>
Step 1: Identify key numbers in the question.
Step 2: Perform necessary calculations to find the result.
</reasoning>
<answer>
Final result: 42
</answer>

Instead of just generating an answer, GRPO rewards the model for explaining its thought process, making its answers more reliable.

Why GRPO is the Future of AI Training

By removing the extra critic model, GRPO helps:

• Lower GPU costs
• Speed up AI training
• Make AI models more scalable

This is why DeepSeek R1’s approach is gaining traction—it allows companies to train AI *without breaking the bank.

The Future: Will PPO Become Obsolete?

For years, Proximal Policy Optimization (PPO) has been the backbone of training AI models like GPT-4, Gemini, and Claude. Its reinforcement learning approach—using a policy model and a critic model—helped shape today’s most powerful AI systems. However, as AI demands grow, so do the costs and limitations of PPO.

The biggest drawback? Inefficiency. PPO trains two models at once, which doubles computational costs and slows down learning. The critic model also adds extra complexity, requiring additional processing power that could be used for scaling AI capabilities instead.

Enter GPRO

With Group Relative Policy Optimization (GRPO) proving itself in models like DeepSeek R1, the AI landscape is shifting toward faster, cheaper, and more scalable training methods. Instead of wasting resources on pairwise comparisons, GRPO ranks multiple responses at once, removing the need for a separate critic model.

So, will PPO become obsolete?

• For traditional AI fine-tuning, PPO might stick around—some models still benefit from its structured learning approach.
• For self-verifying, scalable AI models, GRPO is the future—faster decision-making, lower costs, and stronger AI reasoning make it a game-changer.

Final Thoughts: The Rise of Smarter AI Training

AI is evolving faster than ever, and so are the methods we use to train it. While PPO has served as a reliable training framework, its slow speed, high costs, and dependency on a critic model make it less sustainable for future AI advancements.

By removing the critic model, ranking multiple responses at once, and speeding up AI learning, GRPO is proving to be the next big leap in AI training. Models like DeepSeek R1 are already demonstrating its potential, showing that AI can be trained to verify its own answers, make better decisions, and reduce hallucinations—all while cutting costs.

As AI technology progresses, GRPO’s impact will only grow. Whether it’s for chatbots, search engines, customer support AI, or advanced reasoning models, training methods need to be efficient, scalable, and self-verifying—and GRPO checks all those boxes.

What do you think? Is GRPO the future of AI training, or will PPO still hold its ground? Let’s discuss in the comments!