Worked on the Metta-AI/mettagrid repository to deliver enhanced simulator configuration handling for curriculum training scenarios. Focused on enabling updates to configuration invariants during curriculum runs, this work increased the flexibility and robustness of AI simulation workflows. The approach centered on backend development and configuration management, using Python to implement changes that allow dynamic adaptation of simulation parameters as training progresses. Additionally, performed benchmarking code cleanup to improve the maintainability and reliability of benchmarking tools. No major bugs were addressed during this period, as the primary emphasis was on feature delivery and improving code quality within the simulation and benchmarking infrastructure.