Worked on the instadeepai/Mava repository to enhance the stability of reinforcement learning training pipelines by addressing a critical issue in PPO workflows. Introduced targeted assertions in Python to validate that the number of environments is divisible by the minibatch count across multiple PPO implementations, including mat.py, rec_ippo.py, and rec_mappo.py. This system configuration improvement prevents runtime errors caused by improper batching, resulting in safer and more reliable distributed training. The update also included improvements to repository hygiene through pre-commit hooks, reflecting a focus on correctness, testability, and fault-tolerant design in reinforcement learning system development.