Worked on the rwth-i6/i6_models repository to enhance the LogUniformSampler by introducing a with_replacement option, enabling sampling without replacement for more flexible experimentation. The implementation leveraged Python and PyTorch, specifically utilizing torch.multinomial and direct probability lookups to optimize the sampling path. This approach improved reproducibility and allowed for more robust validation of model variants by reducing sampling bias. Emphasis was placed on maintainability and testability throughout the development process. No major bugs were addressed during this period, with the primary focus on expanding feature capabilities to support more reliable and efficient experimentation pipelines within the existing codebase.