In May 2025, Sahil Jain developed a performance-focused feature for the NVIDIA/NeMo-RL repository, targeting the scalability of distributed reinforcement learning workloads. He reworked the worker initialization process to be asynchronous and parallel, leveraging Python and Ray to introduce futures-based worker creation. By updating the initialization flow to batch-resolve worker references, Sahil reduced startup latency and improved resource utilization during cluster setup. His work in asynchronous programming and distributed systems enabled faster readiness for large-scale training, addressing bottlenecks in startup time. The depth of this contribution lies in its careful refactoring and its impact on efficient, scalable distributed training workflows.