During September 2025, Justin Tong focused on improving the reliability of distributed deep learning workflows in the JustinTong0323/sglang repository. He addressed a critical bug in the BailingMoEModel by refining the initialization logic for word embeddings, ensuring that the enable_dp_attention flag correctly governs tensor parallelism configuration. This adjustment resolved misconfigurations that previously affected data parallel (DP) attention and model scalability. Working primarily in Python and leveraging expertise in distributed systems and model implementation, Justin’s contribution enhanced the stability of DP-enabled Mixture of Experts (MoE) training, supporting more robust and reproducible large-scale experiments for the repository’s users and collaborators.