RTDL is a research repository containing deep learning models specifically designed for tabular data problems. It includes implementations of architectures like MLP, ResNet, FT-Transformer, and the newer TabM with parameter-efficient ensembling. The repository also publishes accompanying research papers with code, covering topics such as optimizer benchmarking, data uncertainty, and foundation model finetuning for tabular domains.