Shengqi focused on improving distributed training stability in the bytedance-iaas/sglang repository by addressing a critical bug in tensor parallelism rank management. Using Python and leveraging expertise in distributed systems, Shengqi modified the initialize_dp_attention function to correctly retrieve the local_rank from the tp_group object, rather than deriving it from tp_rank or tp_size. This targeted fix prevented misrouted attention across processes, reducing instability in multi-node training environments. The work enhanced model convergence consistency and maintainability, streamlining debugging for distributed configurations. Shengqi’s contribution delivered measurable improvements in operational reliability and scalability for large-scale training jobs within the project.