GRPO vs Other RL Algorithms: A Simple, Clear Guide
Reinforcement learning (RL) has transformed how we fineâtune language models. Traditional approaches like Proximal Policy Optimization (PPO) and Trust Region Policy Optimization (TRPO) use a âcriticâ value networkâdoubling model size, memory requirements, and complexity. Meanwhile, humanâalignment methods like Direct Preference Optimization (DPO) optimize for preference, not reasoning.
Group Relative Policy Optimization (GRPO) combines the best of both worlds: it delivers stable, PPOâstyle updates without a criticâcutting memory and compute in halfâwhile specifically boosting reasoning performance in LLMs.
Readerâs Takeaway
By the end, youâll clearly understand:
𧩠What GRPO is â and how it changes the game
đĄ The key differences between REINFORCE(PG), TRPO, PPO, DPO, and GRPO
âïž When (and why) to use each algorithm based on your task
đ§ What Is GRPO?
GRPO is an enhanced variant of Proximal Policy Optimization (PPO) developed by the DeepSeek team to boost mathematical reasoning while cutting memory use. Unlike standard PPO, GRPO:
- Drops the separate value network
Traditional PPO relies on two neural networks:
- a policy network (to choose actions)
- a value network (to estimate expected returns).
GRPO discards the value network entirely. Instead, it computes the advantage baseline directly from aggregated âgroup scoresâ collected across parallel rollouts. This consolidation into a single policy network simplifies the architecture, speeds up forward/backward passes, and reduces inter-network synchronization overhead.
- Reduces resource consumption By eliminating the heavyweight value model, GRPO slashes peak GPU memory requirements by over 40%, freeing up capacity for larger batch sizes or bigger models. Training time also drops because thereâs only one backward pass per update (policy alone), not two. In practice, this translates to faster experiment turnaround and lower cloud compute bills without compromising learning capacity.
- Maintains stable policy updates Stable updates help prevent catastrophic forgetting and promote reliable reasoning improvements. Despite the leaner design, GRPO preserves PPOâs hallmark clipped-objective trust region, ensuring that each policy update remains within a safe divergence bound. This guarantees smooth, monotonic improvements in performance rather than erratic jumps that can destabilize training.
Real-World Results
GRPO achieves comparableâor even superiorâbenchmark performance on mathematical reasoning tasks, while reducing resource use and speeding convergence. Here are some real examples:
đ Code Generation & Coder LLMs
A 1.5B Rust coder model fine-tuned with GRPO saw build success increase from ~60% to ~80%, and unit test passing rates from 22% to 37% on a 15k-example code dataset https://github.com/Oxen-AI/GRPO-With-Cargo-Feedback/tree/main
đ§ AlphaMaze (LLM visual navigation):
In the AlphaMaze project, SFT baseline achieves 86% accuracy on MazeBench; with GRPO fine-tuning, this rises to 93% after just 1,600 steps. The model also exhibits chain-of-thought reasoning and self-correction during navigation.
https://arxiv.org/html/2502.14669
Comparing GRPO with Other RL Algorithms
Having observed how GRPO achieves stronger performance using fewer resources in real-world scenarios, itâs insightful to compare it against other leading reinforcement learning algorithms. The table below summarizes their key differencesâhelping you identify which algorithm aligns best with your specific needs.
Algorithm | Critic Needed? | Computational Cost | Typical Use Case |
REINFORCE (PG) | No | Very low | Simple RL tasks |
TRPO | Yes | Very high | Complex, resourceârich tasks |
PPO | Yes | High | General RL and RLHF |
DPO | No | Medium | Preference tuning LLMs |
GRPO | No | Low | LLM reasoning via RLHF |
Links & Resources
If you want to explore efficient yet powerful RL algorithms, GRPO is definitely worth trying. Here are some key resources and open-source tools to help you get started:
đ HuggingFace TRLâs GRPOTrainer:
The TRL library offers a straightforward implementation of GRPO, making it easier to integrate reinforcement learning into transformer models. The GRPOTrainer class is memory efficient and great for fine-tuning large language models.
Documentation & Tutorial: Explore the official documentation and a hands-on tutorial to understand how to implement GRPO in your projects. https://huggingface.co/docs/trl/main/en/grpo_trainer
đ Colossal-AI GRPO Training Script:
Colossal-AI provides an open-source GRPO implementation tailored for large-scale training. Their training script demonstrates how to fine-tune models using GRPO efficiently.
Training Script: Access the script to see how GRPO can be applied in practice. https://github.com/hpcaitech/ColossalAI/blob/main/applications/ColossalChat/examples/training_scripts/train_grpo.sh
Reference:
[1] DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models
[2] https://github.com/Oxen-AI/GRPO-With-Cargo-Feedback/tree/main
[3] AlphaMaze: Enhancing Large Language Modelsâ Spatial Intelligence via GRPO