Worked on the nvidia-cosmos/cosmos-rl repository to deliver three core features enhancing distributed reinforcement learning workloads. Developed an opt-in NCCL-based payload transfer mechanism in Python, improving throughput and reliability for large data transfers while maintaining backward compatibility and configurability. Strengthened multi-replica training stability by introducing shutdown race tolerance and prompt throttling, reducing crash and out-of-memory risks in asynchronous environments. Added asynchronous rollout-to-rollout weight synchronization using background threads and configurable sync modes, validated with unit tests. The work involved backend development, data processing, and model synchronization, with careful attention to code maintainability through modular refactoring and comprehensive test coverage.