Worked on the meta-llama/llama-cookbook repository to address a key issue in model training observability by refining the validation loss logging mechanism. Implemented a targeted bug fix in Python that ensures validation loss is now recorded for each epoch, rather than only tracking the best value across epochs. This adjustment provides more accurate and truthful loss curves, which are essential for detecting overfitting and evaluating model performance in deep learning workflows. The solution was delivered as a concise, review-friendly change, demonstrating careful attention to code quality, reproducibility, and collaborative development practices in machine learning and model training environments.