During their work on the instadeepai/Mava repository, Fratin Okek focused on improving the stability of reinforcement learning training pipelines by addressing a critical issue in PPO workflows. They implemented environment minibatch divisibility validation across multiple PPO system files, using Python to add targeted assertions that ensure the number of environments is always divisible by the minibatch count. This approach prevents runtime errors caused by improper batching and enhances the reliability of distributed training. Fratin also contributed to repository hygiene by updating pre-commit hooks, demonstrating attention to system configuration and correctness while reducing debugging time for reinforcement learning practitioners.