How GRPO’s Relative Rewards Work
Fine-tuning large language models (LLMs) to align with human preferences is tough—but a new technique called GRPO (Group Relative Policy Optimization) is changing the game by making training simpler, faster, and more memory-efficient.
Why Move Beyond PPO?
Traditional reinforcement learning methods like PPO rely on a critic network (a separate “value model”) to estimate how good each action is. While effective, this adds complexity, memory overhead, and training instability—especially for massive models.
GRPO cuts the critic. Instead, it uses relative comparisons within a group of responses to guide learning—no extra model needed.
The Core Idea: Learn From the Group
Here’s how it works:
Generate multiple responses to the same prompt (e.g., 4 different code solutions).
Score each response using a reward model (e.g., based on correctness, completeness, or human feedback).
Normalize rewards using the group’s average and standard deviation—this becomes the relative advantage.
Update the policy:
Responses above average get reinforced.
Responses below average are discouraged.
A Quick Example:
Imagine you’re training an AI to solve math problems. You ask it:
“What is 17 × 24? Show your reasoning.”
Instead of generating just one answer, the model produces four different attempts:
One simply says, “408. I used a calculator.”
Another carefully breaks it down: “17 × 20 = 340, 17 × 4 = 68, so 340 + 68 = 408.”
A third guesses incorrectly: “380.”
And a fourth uses a clever trick: “17 × 25 = 425, minus 17 = 408.”
Now, a reward model—trained on human feedback or correctness—assigns scores based on accuracy, clarity, and reasoning quality. The “calculator” answer gets a low score (it’s correct but lazy), the wrong answer gets zero, and the two thoughtful solutions earn high marks.
Here’s where GRPO (Group Relative Policy Optimization) shines.
Instead of treating each score in isolation, GRPO looks at the whole group. It calculates the average reward across all four responses—say, 0.65—and uses that as a baseline. Then, it measures how much better or worse each answer is compared to that average.
The clever and the step-by-step answers now stand out as clearly above average—so the model learns: “This is the kind of response I should favor.”
Meanwhile, the mere “408” answer, while technically correct, falls below the group average because others did more. The model learns: “Just being right isn’t enough—I need to be helpful, too.”
And the wrong answer? It’s heavily discouraged.
Crucially, GRPO does all this without a separate ‘critic’ network—unlike older methods like PPO, which need extra memory and computation to estimate value. GRPO uses the group itself as its own reference point.
This relative comparison creates stronger, more nuanced learning signals. The AI doesn’t just chase high scores—it learns to outperform its own alternatives, pushing toward responses that are not only correct but also clear, creative, and complete.
In real-world applications—from coding assistants to math tutors—this means models that don’t just answer, but explain, adapt, and improve based on context-aware feedback.
And all of it happens with less memory, less complexity, and more intelligence.
That’s the power of GRPO: teaching AI to raise its own bar—by comparing its best to its worst, and everything in between.