Worked on stabilizing data flow in the inclusionAI/AReaL repository by addressing a bug related to attention_mask shape integrity during padding removal. Using Python and leveraging skills in data processing and machine learning utilities, ensured that the attention_mask maintained correct dimensions throughout downstream slicing and attention computations. This fix reduced the risk of data processing errors and improved model reliability in production environments. The solution included adding targeted tests for edge cases involving attention_mask padding, which helped prevent future regressions. All changes were isolated to minimize performance impact and underwent thorough review, reflecting a careful and methodical engineering approach.