title: Table-R1: Inference-Time Scaling for Table Reasoning
summary: 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.
paper_authors._id: 68391925d73e6015a1b0f305
paper_authors.name: Zheyuan Yang
paper_authors._id: 68391925d73e6015a1b0f306
paper_authors.name: Lyuhao Chen
paper_authors._id: 68391925d73e6015a1b0f307
paper_authors.name: Arman Cohan
paper_authors._id: 68391925d73e6015a1b0f308
paper_authors.name: Yilun Zhao
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-05-30T06:53:43.117Z
paper_authors._id: 62f662bcc58915315c4eccea
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/62f662bcc58915315c4eccea/zOAQLONfMP88zr70sxHK-.jpeg
paper_authors.fullname: Yilun Zhao
paper_authors.type: user
paper_authors.user: yilunzhao
paper_title: Table-R1: Inference-Time Scaling for Table Reasoning
paper_summary: 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.
paper_ai_summary: Two post-training strategies, distillation and RLVR, enable inference-time scaling in table reasoning tasks, resulting in a model (Table-R1-Zero) that matches GPT-4.1's performance using fewer parameters and shows strong generalization.
paper_ai_keywords: distillation reinforcement learning verifiable rewards RLVR reasoning traces DeepSeek-R1 LLMs Table-R1-SFT GRPO Table-R1-Zero short-form QA fact verification free-form QA instruction tuning model architecture choices cross-task generalization table reasoning skills