Worked on the graphcore/pytorch-fork repository to address a targeted issue in the CooperativeReduction test suite, focusing on improving the reliability of Float16 input validation. Applied Python and CUDA skills to introduce a higher-precision comparison mechanism for Float16 values, which reduced flaky continuous integration failures and stabilized the FP16 pathway verification process. The solution involved enhancing the test logic to ensure more accurate detection of correctness issues in machine learning workflows. By linking the fix to a tracked issue and formalizing it through a dedicated commit, contributed a focused quality improvement that reinforces the robustness of automated testing for FP16 operations.