Group Relative Policy Optimization (GRPO): A Critic-Free Approach for Decentralized Model Training

Group Relative Policy Optimization (GRPO) in Large-Scale Model Training

Where Did GRPO Come From?

In the world of reinforcement learning, it’s always been a challenge to scale up and keep things stable, especially when you’re fine-tuning large models like LLMs or trying to harness distributed, sometimes unreliable, compute. For years, most projects defaulted to classic algorithms like PPO (Proximal Policy Optimization). PPO has been a workhorse—used everywhere from RLHF for language models to robotics—mainly because it’s fairly robust and isn’t as brittle as its predecessors.

But as models got bigger, some core problems stood out: running a separate “critic” value network (required for PPO) can double your memory and compute requirements. For long-context LLMs, maintaining and updating a critic often becomes a bottleneck, not to mention it introduces a slew of tuning issues. Researchers at DeepSeek, working on high-performance math LLMs, hit these very roadblocks and decided to rethink the process.

Their answer, introduced in 2024 and now showing up in both DeepSeekMath and DeepSeek-R1, was Group Relative Policy Optimization (GRPO). It’s a subtle but smart shift: instead of using a value network to judge how good each output is, why not compare multiple outputs against each other for the same prompt and use the group average as a baseline? This critic-free approach fits naturally with how a lot of RLHF reward models are trained—by comparing and ranking samples. You can skip training an extra neural net and just let the data “tell you” which responses are better.

What Makes GRPO Different?

The core idea is surprisingly straightforward once you see it: instead of asking “Is this output good, according to my value model?”, GRPO asks “How does this output compare to its peers for the same prompt?” That means you sample multiple outputs per prompt, score them all, and then use their average score as your baseline. Outputs above the mean get a positive update; those below, a negative one. All of the machinery for stable PPO training—clipped objectives, KL regularization, using a reference policy—stays in place. But there’s no critic. This makes the training loop simpler, cheaper, and much less finicky to debug.

A typical GRPO loop looks like this:

1. For each input, generate a group of candidate responses.
2. Use a reward function (could be a learned reward model, could be something programmatic) to score each output.
3. Compute the group’s average score. For each response, calculate its advantage as (reward - average).
4. Use the standard PPO-style clipped objective, but now with these group-relative advantages.
5. Add a KL penalty term so your policy doesn’t drift too far from your starting point.
6. Update the model via gradient ascent. No value network, no extra backprop, just the policy.

This approach is elegant and, crucially, works well for LLMs. It’s closer to how human feedback is actually delivered (“choose the best response among these” vs. “score each one in isolation”), and it removes an entire class of implementation headaches.

GRPO and Decentralized Compute: Why Gensyn Uses It

Gensyn.ai has a particular interest in algorithms like GRPO because their core product is decentralized compute. Imagine hundreds or thousands of different machines—some in data centers, some in people’s homes—collaborating to train a model over a blockchain-backed network. Traditional RL methods, especially those that require lots of inter-node communication or centralized value networks, just aren’t a good fit.

GRPO, on the other hand, is almost tailor-made for this world. Here’s why:

• Local Advantage Calculation: Because everything’s relative within a group, nodes don’t need to synchronize a global value model. They just need to share candidate outputs and rewards for each prompt.
• Resource Efficiency: With no critic to train or synchronize, memory and compute requirements are lower. Even mid-range consumer GPUs can contribute meaningfully, opening the door to a more inclusive network.
• Decentralized Peer Review: In Gensyn’s RL Swarm approach, each node is both a policy learner and a “critic” for others. After every round, nodes exchange outputs, provide feedback, and compute rewards based on group consensus or correctness. The reward baseline is the average of all the group’s outputs.
• Easier Verification: On a blockchain, it’s important to verify work without trusting any single party. Since GRPO only requires checking outputs, rewards, and policy updates (not the internals of a value model), it’s relatively easy for third parties to audit or challenge contributions.

Put simply, GRPO’s design lets a bunch of independent nodes train together, leveraging their diversity while keeping the coordination overhead manageable. This is a huge step forward for distributed, trustless machine learning.

A Closer Look: How It Works in Practice

Let’s break down the technical flow, based on both DeepSeek’s papers and Gensyn’s RL Swarm implementation:

• Sampling: Each node gets a prompt and generates multiple candidate answers (in Gensyn’s system, sometimes each node is itself one “candidate” in the group).
• Scoring: Each output is evaluated—this could be by a learned reward model, by peers voting, or by automated checks (unit tests, consensus, etc.).
• Baseline and Advantage: Compute the mean reward for the group. Each node’s advantage is simply its score minus the mean.
• Update: Using the clipped PPO objective and a KL penalty, nodes update their models to favor outputs that beat the average.
• (Optional) Sync: Occasionally, nodes sync up weights or share updates, but this can be much less frequent than in classic distributed RL.

This process is naturally robust to dropped nodes (if one participant drops out, the rest can continue), and because advantage calculation is local to each group, you don’t need a central server or a tightly coupled network.

What’s Actually New Here?

If you’ve worked in RL, you might wonder if group-based relative rewards are just “tricks” or if there’s a real improvement. The answer is: yes, it’s both mathematically valid and practical. By using a relative baseline, variance is controlled (a key problem in policy gradients), and the rewards are always grounded in actual, observable data—not an imperfect critic that might go off the rails. Plus, for distributed or blockchain-backed systems, verification becomes orders of magnitude simpler.

DeepSeek’s original papers, as well as open-source reproductions and Gensyn’s public demos, show that you can fine-tune surprisingly large models with less compute, less memory, and less code than before. For anyone running RLHF on the open internet, this isn’t just a minor improvement; it could be the unlock needed to scale up in a decentralized way.

Why It Matters for Blockchain-Based Compute

There’s a bigger picture here. If we’re serious about building global, open machine learning—where anyone can participate and contribute compute, and where rewards can be distributed fairly and transparently—algorithms like GRPO are foundational.

• Parallelism: Each node can work independently for most of the process.
• Easy Verification: Blockchain verifiers can check outputs, rewards, and model updates with minimal data.
• Resource Inclusivity: More devices, not just top-tier servers, can participate.
• Trustless Collaboration: The protocol itself defines “success,” and verification is public.

It’s still early days, but the combination of GRPO’s efficiency and blockchain-based coordination could radically reshape how—and where—frontier models get trained.

Final Thoughts

Group Relative Policy Optimization might sound like a small tweak to PPO, but in practice, it’s an important step towards making RL tractable at scale—especially in decentralized, trustless environments. By eliminating the critic and making everything relative, GRPO streamlines both training and verification. In systems like Gensyn’s RL Swarm, it becomes the backbone for collaborative, crowd-powered model improvement.

If you’re interested in the details, I recommend checking out DeepSeek’s papers and Gensyn’s open-source RL Swarm codebase. Watching how these ideas move from theory to large-scale, real-world training is one of the most exciting stories in machine learning right now.

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