Understanding GRPO

Group Relative Policy Optimization (GRPO) represents a significant simplification in how we train language models with reinforcement learning. The key insight? We don’t actually need a learned value function.

The Problem with Traditional RL

In standard policy gradient methods like PPO, we maintain two neural networks:

1. Policy network: Decides what action to take
2. Value network: Estimates expected future reward

The value network is used to compute advantages—how much better an action was compared to what we expected. But this introduces challenges:

• Training instability from two competing objectives
• Increased memory and compute requirements
• Potential for value estimation errors to corrupt policy updates

GRPO’s Solution

GRPO sidesteps the value function entirely by using group-relative baselines. Instead of asking “how much better was this action than expected?”, it asks “how much better was this response than the others in this batch?”

# Simplified GRPO advantage computation
def compute_grpo_advantages(rewards, group_size):
    # Reshape into groups
    grouped = rewards.reshape(-1, group_size)

    # Subtract group mean (the baseline)
    group_means = grouped.mean(dim=-1, keepdim=True)
    advantages = grouped - group_means

    return advantages.flatten()

This is elegant for several reasons:

1. No learned component: The baseline is a simple statistic
2. Automatic scaling: Advantages are always relative to the batch
3. Reduced variance: Comparing within groups controls for prompt difficulty

Implications for LLM Training

For language model fine-tuning, GRPO means we can:

• Run more experiments with the same compute budget
• Avoid value head initialization issues
• Simplify the training loop significantly

The tradeoff is that we need multiple completions per prompt, but for LLM training this is often desirable anyway for diversity.