Developed FA4 Attention support within the ring attention module for the PrimeIntellect-ai/prime-rl repository, enabling parallel training and improved scalability for reinforcement learning workloads. The work involved implementing FA4 forward and backward wrappers and creating a custom autograd function to support variable-length sequences. By integrating FA4 routing into key components and aligning the approach with the existing FA3 ring attention pattern, the solution facilitated efficient all-gather and reduce-scatter operations for large-scale distributed training. Leveraging Python, PyTorch, and distributed computing techniques, this feature expanded context length and throughput, enhancing the model’s ability to handle longer sequences in demanding RL environments.