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stanford-star/
rt-v1

Name: rt-v1
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Relational Transformer

This repository contains the official checkpoints for the Relational Transformer (RT), introduced in the paper Relational Transformer: Toward Zero-Shot Foundation Models for Relational Data.

Relational Transformer is a foundation model architecture designed to be pretrained on diverse relational databases and applied to unseen datasets and tasks without task- or dataset-specific fine-tuning. It utilizes a novel Relational Attention mechanism over columns, rows, and primary-foreign key links.

Paper: Relational Transformer: Toward Zero-Shot Foundation Models for Relational Data
GitHub Repository: snap-stanford/relational-transformer

Installation

The repository uses pixi for package management.

bash

git clone https://github.com/snap-stanford/relational-transformer
cd relational-transformer
pixi install
# compile and install the rust sampler
cd rustler
pixi run maturin develop --uv --release

Checkpoints

The project provides two types of checkpoints:

pretrain_<dataset>_<task>.pt: Pretrained with the specified <dataset> held out.
contd-pretrain_<dataset>_<task>.pt: Obtained by continued pretraining on <dataset> with the specific <task> held out.

You can download specific checkpoints using the Hugging Face CLI:

bash

mkdir -p ~/scratch/rt_ckpts
huggingface-cli download rishabh-ranjan/relational-transformer \
  --repo-type model \
  --include "pretrain_rel-amazon_user-churn.pt" \
  --local-dir ~/scratch/rt_ckpts \
  --local-dir-use-symlinks False

Usage

To use these checkpoints, pass the path to the load_ckpt_path argument in the training scripts provided in the GitHub repository. For example, to run a finetuning experiment:

pixi run torchrun --standalone --nproc_per_node=8 scripts/example_finetune.py

RelBench leaderboard checkpoints (added 2026-06)

These files back the RT numbers on the RelBench leaderboard. Protocols follow the repo scripts, with one change: regression best-checkpoint selection uses val NMAE (MAE / train-split std, ddof=1) — the leaderboard metric — instead of R². Evaluation = full official test split (AUROC / NMAE).

pretrain_rel-event_<task>.pt — leave-rel-event-out pretraining (50k steps), per-task best. rel-event was not covered in the original release; these produce the RT zero-shot rel-event cells.
contd-pretrain_rel-event_<task>.pt — continued pretraining on the other rel-event tasks from the matching pretrain checkpoint (2^12+1 steps).
finetune-from-{pretrain,contd-pretrain}_<db>_<task>.pt — the fine-tuned checkpoint behind each replicated "RT | pretrained + fine-tuned" leaderboard cell. The board takes the per-task best over fine-tuning from the plain-pretraining vs continued-pretraining init (init treated as a hyperparameter); the file present is the winning init for that task. Cells without a file here are the paper's own pretrain-init fine-tuning numbers — reproduce those with scripts/example_finetune.py from the matching pretrain_<db>_<task>.pt.

Citation

bibtex

@inproceedings{ranjan2025relationaltransformer,
    title={{Relational Transformer:} Toward Zero-Shot Foundation Models for Relational Data}, 
    author={Rishabh Ranjan and Valter Hudovernik and Mark Znidar and Charilaos Kanatsoulis and Roshan Upendra and Mahmoud Mohammadi and Joe Meyer and Tom Palczewski and Carlos Guestrin and Jure Leskovec},
    booktitle={The Fourteenth International Conference on Learning Representations},
    year={2026}
}

stanford-star/rt-v1