Worked on enhancing the reliability of the upstash/FlagEmbedding repository by addressing a critical issue in the model distillation training workflow. Focused on correcting the use of dense vectors during group size calculations, the work ensured that the training logic consistently used the correct vectors across all batches. This fix improved the integrity and reproducibility of the training process, reducing variance in downstream embeddings and minimizing debugging time. Leveraged Python and deep learning techniques to harden the training loop, with clear commit traceability supporting future audits. No new features were added, emphasizing stability and correctness in machine learning model development.