Table-R1: Inference-Time Scaling for Table Reasoning
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.
68391925d73e6015a1b0f305
Zheyuan Yang
68391925d73e6015a1b0f306
Lyuhao Chen
68391925d73e6015a1b0f307
Arman Cohan
68391925d73e6015a1b0f308
Yilun Zhao
claimed_verified
2025-05-30T06:53:43.117Z
62f662bcc58915315c4eccea
https://cdn-avatars.huggingface.co/v1/production/uploads/62f662bcc58915315c4eccea/zOAQLONfMP88zr70sxHK-.jpeg
Yilun Zhao
user
yilunzhao
Table-R1: Inference-Time Scaling for Table Reasoning
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.
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.
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