Worked on the PrimeIntellect-ai/prime-rl repository to improve the robustness of the model training pipeline, focusing on handling edge cases in data processing. Addressed a specific issue where missing completion temperature values could cause training failures by implementing a default value within the prepare_sample function. This change ensured that training runs remained stable even when certain parameters were absent, reducing the likelihood of downstream errors and minimizing debugging time. Utilized Python and applied machine learning principles to enhance the reliability of experiment workflows. The work demonstrated careful attention to pipeline stability and contributed to more resilient model development processes.