Alena Wang focused on stabilizing swiglu patching for Llama4 Mixture-of-Experts (MoE) layers in LinkedIn’s Liger-Kernel repository. She implemented a targeted patching approach that applies updates to the shared_expert module within MoE layers and directly to non-MoE layers, addressing patching misconfigurations and reducing runtime risks. By updating the default swiglu parameter to True, Alena aligned the configuration with the new patching logic, supporting safer deployment and experimentation with Llama4 architectures. Her work leveraged deep learning, model optimization, and Python, demonstrating a thoughtful approach to improving reliability and maintainability in complex transformer model environments.