This month focused on stabilizing SGLang and vLLM rollout servers by aligning maximum position embeddings handling with the maximum model length across Verl deployments and the latest software (vLLM 0.12). A single critical bug fix was implemented to ensure correct retrieval of maximum position embeddings from the model configuration, enabling servers to operate with the maximum model length and maintain compatibility with ongoing software updates. This work reduces risk of embedding misalignment for long inputs and enhances reliability of rollout deployments across environments.

Delivered Round-robin Expert Placement Strategy for MoE in vLLM, adding a new round_robin option to complement the linear strategy and improve load balancing across ranks. Implemented the placement logic in determine_expert_map and added comprehensive tests across configurations and edge cases. This work enhances throughput and resource utilization for MoE workloads and reduces regression risk through stronger test coverage, supporting safer deployments. Demonstrated skills in MoE architectures, vLLM internals, Python, and test-driven development.