<ul><li>Group Relative Policy Optimization (GRPO) enhances policy learning by computing gradients from relative comparisons among candidate outputs sharing a common input prefix.</li><li>Prefix Grouper is an efficient GRPO training algorithm that eliminates redundant prefix computation via a Shared-Prefix Forward strategy, reducing computational overhead in long shared-prefix scenarios.</li><li>Implemented by restructuring self-attention into two parts, Prefix Grouper encodes the shared prefix only once while maintaining full differentiability and compatibility with end-to-end training.</li><li>Empirical evidence shows that Prefix Grouper achieves training-equivalence to standard GRPO, reducing computational costs significantly and improving scalability for more complex tasks and larger models.</li></ul>

Prefix Grouper: Efficient GRPO Training through Shared-Prefix Forward

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