Worked on stabilizing long-sequence processing in the NVIDIA-NeMo/RL repository, focusing on DeepScaler’s memory management and numerical stability. Addressed critical Out-Of-Memory issues for sequence lengths up to 16,000 and 24,000 tokens by optimizing memory usage, including explicit deletion of intermediate tensors after use. Improved numerical stability and memory efficiency by casting logits to float32 before log-probability calculations. These changes enhanced production reliability for long-context inference and laid the foundation for future scalability improvements. The work leveraged deep learning and model optimization skills, utilizing Python and YAML to deliver measurable technical improvements and support more robust production workloads.