Table-R1: Inference-Time Scaling for Table Reasoning

• URL: http://arxiv.org/abs/2505.23621v1
• Date: Thu, 29 May 2025 16:28:50 GMT
• Title: Table-R1: Inference-Time Scaling for Table Reasoning
• Authors: Zheyuan Yang, Lyuhao Chen, Arman Cohan, Yilun Zhao,
• Abstract summary: We develop and evaluate two post-training strategies to enable inference-time scaling.<n>For distillation, we introduce a large-scale dataset of reasoning traces generated by DeepSeek-R1.<n>For RLVR, we propose task-specific verifiable reward functions and apply the GRPO algorithm to obtain the Table-R1-Zero model.
• Score: 25.481170375825812
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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