Group Relative Policy Optimization (GRPO) is a novel reinforcement learning method introduced in the DeepSeekMath paper that aims to enhance mathematical reasoning capabilities while reducing memory consumption. GRPO builds upon the Proximal Policy Optimization (PPO) framework and offers several advantages for tasks requiring advanced mathematical reasoning.
The implementation of GRPO involves generating multiple outputs for each input question, scoring these outputs using a reward model, computing advantages based on the rewards, and updating the policy to maximize the GRPO objective. This method eliminates the need for a value function model, reducing memory and computational complexity by using group scores to estimate the baseline.
GRPO introduces innovative features such as a simplified training process, integration of the KL divergence term into the loss function, and significant performance improvements in mathematical benchmarks. It differentiates itself from other methods by its iterative approach to training reward models, which helps fine-tune the model more effectively.
When applied to DeepSeekMath, GRPO demonstrated substantial improvements in in- and out-of-domain tasks during the reinforcement learning phase. The method’s ability to enhance performance without relying on a separate value function showcases its potential for broader applications in reinforcement learning scenarios.
In conclusion, GRPO is a promising advancement in reinforcement learning methods tailored for mathematical reasoning, offering efficient resource utilization and innovative techniques for computing advantages. Its application in DeepSeekMath highlights its potential to enhance the capabilities of language models in complex, structured tasks like mathematics.
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