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Table-R1: Region-based Reinforcement Learning for Table Understanding

Paper · 2026 · Zhenhe Wu, Jian Yang, Zhongjiang He et al.

Summary

Large language models still have difficulty answering questions over tables because answers depend on how rows and columns relate. Prompting methods such as CoT and PoT help, but few training methods are designed around table structure. The authors propose Table-R1, a two-stage framework that teaches a model to first locate the part of the table it needs (a "table region", given as a set of columns plus a set of row indices) and then reason toward the answer. In the first stage, RE-SFT, the model is fine-tuned on reasoning traces that contain the region partway through, across several reasoning styles (textual, symbolic and program-based). In the second stage, TARPO, a GRPO variant scores both region accuracy and answer correctness. The region's share of the reward decreases during training, and outputs whose region and answer signals disagree are penalized. Across several base models and three benchmarks, Table-R1 improves scores by 14.36 points on average and uses far fewer reasoning tokens than GRPO. This suggests that intermediate structural evidence is a useful reward signal beyond the final answer.

Key points

  • Regions are defined by columns and rows rather than individual cells. This saves input tokens and keeps a sub-table layout, and it outperformed a cell-based variant in both score and token use.
  • TARPO's region reward is the mean of column-level and row-level IoU against the gold region. The answer reward is binary: exact match for numbers, a Rouge-L threshold for strings.
  • Reported gains are +14.36 points on average across base models and three benchmarks, including TableBench. TARPO cuts reasoning tokens by 67.5% relative to GRPO.
  • The authors release TableInstruct-RE, a version of TableInstruct whose CoT traces include explicit table-region annotations.
  • Limitations: experiments cover only 3B–8B models (e.g., Qwen3-8B) with 8192 input / 2048 output token limits. Industrial tables and multi-table QA were not tested.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

 Institutions

🎓Beihang UniversityBeijing · China86 works · 9,097 citations🏢China TelecomBeijing · China9 works · 16 citations🏢China Telecom (China)Changsha · China8 works · 15 citations

 Fields, topics and concepts

📚 Shelves 5The field of science this work belongs to (OpenAlex fields).Computer Science · 18,614 worksArtificial Intelligence · 17,965 worksArts and Humanities · 17,450 worksLanguage and Linguistics · 17,296 worksComputer Vision and Pattern Recognition · 3,486 works

Cite

Cite as Zhenhe Wu, Jian Yang, Zhongjiang He, Changzai Pan, Jiaheng Liu, Xianjie Wu, Yu Zhao, Shuangyong Song, Yongxiang Li, Zhoujun Li and Xuelong Li. 2026. Table-R1: Region-based Reinforcement Learning for Table Understanding. Findings of the Association for Computational Linguistics: ACL 2026.

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.18653/v1/2026.findings-acl.1364

@inproceedings{wu2026table,
  title = {{Table-R1: Region-based Reinforcement Learning for Table Understanding}},
  author = {Zhenhe Wu, Jian Yang, Zhongjiang He, Changzai Pan, Jiaheng Liu, Xianjie Wu, Yu Zhao, Shuangyong Song, Yongxiang Li, Zhoujun Li and Xuelong Li},
  booktitle = {Findings of the Association for Computational Linguistics: ACL 2026},
  year = {2026},
  doi = {10.18653/v1/2026.findings-acl.1364},
  url = {https://doi.org/10.18653/v1/2026.findings-acl.1364}
}

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