Table-R1: Inference-Time Scaling for Table Reasoning
Authors: name: Zheyuan Yang, name: Lyuhao Chen, name: Arman Cohan, name: Yilun Zhao

In this work, we present the first study to explore inference-time scaling on
table reasoning tasks. We develop and evaluate two post-training strategies to
enable inference-time scaling: distillation from frontier model reasoning
traces and reinforcement learning with verifiable rewards (RLVR). For
distillation, we introduce a large-scale dataset of reasoning traces generated
by DeepSeek-R1, which we use to fine-tune LLMs into the Table-R1-SFT model. For
RLVR, we propose task-specific verifiable reward functions and apply the GRPO
algorithm to obtain the Table-R1-Zero model. We evaluate our Table-R1-series
models across diverse table reasoning tasks, including short-form QA, fact
verification, and free-form QA. Notably, the Table-R1-Zero model matches or
exceeds the performance of GPT-4.1 and DeepSeek-R1, while using only a
7B-parameter LLM. It also demonstrates strong generalization to out-of-domain
datasets. Extensive ablation and qualitative analyses reveal the benefits of
instruction tuning, model architecture choices, and cross-task generalization,
as well as emergence of essential table reasoning skills during RL training.