🤗 Hugging Face | 📄 arXiv
Table-R1 introduces the first systematic study of inference-time scaling for table reasoning tasks. We develop two post-training strategies: distillation from frontier model reasoning traces and reinforcement learning with verifiable rewards (RLVR). Our 7B-parameter Table-R1-Zero model matches or surpasses GPT-4.1 and DeepSeek-R1 performance while exhibiting strong generalization to out-of-domain datasets.
Overview of the Table-R1.
git clone https://github.com/Table-R1/Table-R1.git # Install verl framework cd Table-R1/verl pip install -e . cd .. pip install -r requirements.txt
# Prepare SFT dataset python data/table-r1-sft.py # Run SFT training bash script/table-r1-sft.sh
# Prepare RLVR dataset python data/table-r1-zero.py # Run RLVR training bash script/table-r1-zero.sh
# Prepare evaluation dataset python data/table-r1-eval.py # Run evaluation bash script/table-r1-eval.sh
- All models are trained using the excellent verl framework
If you find Table-R1 useful in your research, please cite our paper:
@article{yang2025tabler1, title={Table-R1: Inference-Time Scaling for Table Reasoning}, author={Yang, Zheyuan and Chen, Lyuhao and Cohan, Arman and Zhao, Yilun}, journal={arXiv preprint arXiv:2505.23621}, year={2025} }