In September 2025, Luca Pasqualin integrated Distributed Checkpointing (DCP) for weight synchronization in the meta-pytorch/forge repository, focusing on scalable distributed training. He introduced a use_dcp flag to control DCP usage and updated the weight loading and saving logic within the policy and trainer modules to be DCP-aware. This approach ensures consistent checkpointing across distributed workers, simplifying the enablement of DCP in production environments. Working primarily with Python and leveraging expertise in distributed systems and machine learning operations, Luca delivered a foundational feature that enhances robustness and scalability for distributed PyTorch workflows, demonstrating depth in both design and implementation.