Soodoshll focused on stabilizing long-sequence processing in the NVIDIA-NeMo/RL repository, addressing critical memory management and numerical stability issues in DeepScaler. By optimizing memory usage, including explicit deletion of intermediate tensors and casting logits to float32 before log-probability calculations, Soodoshll reduced out-of-memory risks for sequence lengths up to 24,000 tokens. The work, implemented in Python and YAML, improved production reliability for long-context inference and laid the foundation for future scalability. Drawing on expertise in deep learning, large language models, and model optimization, Soodoshll delivered targeted engineering solutions that enhanced the robustness and efficiency of production workloads in this domain.