Prefix Grouper: Efficient GRPO Training through Shared-Prefix Forward
Authors: name: Zikang Liu, name: Tongtian Yue, name: Yepeng Tang, name: Longteng Guo, name: Junxian Cai, name: Qingbin Liu, name: Xi Chen, name: Jing Liu

Group Relative Policy Optimization (GRPO) enhances policy learning by
computing gradients from relative comparisons among candidate outputs that
share a common input prefix. Despite its effectiveness, GRPO introduces
substantial computational overhead when processing long shared prefixes, which
must be redundantly encoded for each group member. This inefficiency becomes a
major scalability bottleneck in long-context learning scenarios. We propose
Prefix Grouper, an efficient GRPO training algorithm that eliminates redundant
prefix computation via a Shared-Prefix Forward strategy. In particular, by
restructuring self-attention into two parts, our method enables the shared
prefix to be encoded only once, while preserving full differentiability and
compatibility with end-to-end training. We provide both theoretical and
empirical evidence that Prefix Grouper is training-equivalent to standard GRPO:
it yields identical forward outputs and backward gradients, ensuring that the
optimization dynamics and final policy performance remain unchanged.
Empirically, our experiments confirm that Prefix Grouper achieves consistent
results while significantly reducing the computational cost of training,
particularly in long-prefix scenarios. The proposed method is fully
plug-and-play: it is compatible with existing GRPO-based architectures and can
be seamlessly integrated into current training pipelines as a drop-in
replacement, requiring no structural modifications and only minimal changes to
input construction and attention computation. Prefix Grouper enables the use of
larger group sizes under the same computational budget, thereby improving the
scalability of GRPO to more complex tasks and larger models. Code is now
available at https://github.com/johncaged/PrefixGrouper