Contributed to NVIDIA/Megatron-LM by developing core improvements for transformer reinforcement learning training, focusing on memory efficiency and distributed throughput. Introduced PackedSeqParams and rewrote sequence packing logic to enhance pipeline parallelism, ensuring robust handling of variable-length sequences and stable reduce-scatter operations in tensor-parallel setups. Addressed edge cases in sequence padding and integrated attention masks for consistent parallelism behavior. Additionally, implemented a Safe Inference guard for dummy_forward to prevent inappropriate cudagraph execution, improving inference reliability and deployment predictability. Work was primarily done in Python and CUDA, leveraging deep learning, parallel computing, and reinforcement learning expertise to deliver production-ready solutions.