Worked on improving numerical stability and test coverage in the huggingface/trl repository, focusing on distillation with variable-length inputs. Addressed a backward-pass issue where -inf teacher log probabilities during intra-batch padding could cause NaN or Inf values to leak, by replacing -inf with zero to ensure stable training. Enhanced robustness by adding unit tests that validate correct handling of variable-length sequences in the distillation process, and connected these changes to the reverse-KL server path. Utilized Python, PyTorch, and deep learning techniques to deliver more reproducible experiments and facilitate future maintenance through improved documentation and comprehensive test coverage.