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

Changzai Pan; Jiaheng Liu; Jian Yang; Jie Zhang; Shuangyong Song; Xianjie Wu; Xueling Li; Yongxiang Li; Yu Zhao; Zhenhe Wu

arxiv: 2505.12415 · v3 · pith:Y27B7XT6new · submitted 2025-05-18 · 💻 cs.CL · cs.AI

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

Zhenhe Wu , Jian Yang , Zhongjiang He , Changzai Pan , Jie Zhang , Jiaheng Liu , Xianjie Wu , Yu Zhao

show 4 more authors

Shuangyong Song Yongxiang Li Zhoujun Li Xueling Li

This is my paper

Pith reviewed 2026-05-22 14:19 UTC · model grok-4.3

classification 💻 cs.CL cs.AI

keywords table understandingreinforcement learningregion-based reasoningtable question answeringlanguage modelssupervised fine-tuningpolicy optimizationtabular reasoning

0 comments

The pith

Focusing language models on relevant table regions during reasoning boosts table question answering by 14 points on average.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper shows that large language models struggle with tables because they must navigate row-column structures without clear guidance on which parts matter. By first training models to locate key regions and then using a reinforcement learning method that rewards both accurate region identification and correct final answers, performance rises sharply across different base models. This approach also cuts the length of model responses by more than half compared with standard reinforcement learning. A sympathetic reader would care because tables appear in finance, science, and everyday data tasks, and better table reasoning could make smaller, cheaper models reliable for these jobs.

Core claim

Table-R1 combines Region-Enhanced Supervised Fine-Tuning to teach models to identify relevant table regions before answering, with Table-Aware Group Relative Policy Optimization that mixes region accuracy rewards and answer correctness rewards, using decay on region rewards and penalties for inconsistent reasoning steps, resulting in higher accuracy and shorter outputs on table benchmarks.

What carries the argument

Table-Aware Group Relative Policy Optimization (TARPO), which dynamically balances rewards for correct table region identification against rewards for correct answers while applying decaying weights and consistency penalties to keep reasoning aligned with table structure.

If this is right

Smaller base models reach accuracy levels that previously required models ten times larger.
Response token use drops by roughly two-thirds while accuracy rises.
Reasoning steps become more consistent with the actual structure of the input table.
The same training recipe works across several different starting language models.
Table understanding improves without needing larger models or more data at inference time.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

The same region-first idea could help models handle other structured inputs such as spreadsheets or database query results.
If the region identification step generalizes, it may reduce the need for very long context windows when processing large tables.
Real applications in data analysis tools might see both higher reliability and lower compute cost.
Extending the reward balance to include checks for numerical accuracy inside cells could further strengthen performance on calculation-heavy questions.

Load-bearing premise

The gains depend on the idea that explicitly teaching models to pick out important table regions first will improve final answer quality even when the tables and questions differ from those seen during training.

What would settle it

Run the trained models on a fresh collection of table question-answering examples drawn from domains outside the three benchmarks used in the paper and measure whether accuracy gains and token reductions hold or disappear.

Figures

Figures reproduced from arXiv: 2505.12415 by Changzai Pan, Jiaheng Liu, Jian Yang, Jie Zhang, Shuangyong Song, Xianjie Wu, Xueling Li, Yongxiang Li, Yu Zhao, Zhenhe Wu, Zhongjiang He, Zhoujun Li.

**Figure 2.** Figure 2: The framework of Table-R1. In RE-SFT , we incorporate the minimum table region at the [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗

**Figure 3.** Figure 3: Data statistics of reinforcement learning training on Qwen3-8B. [PITH_FULL_IMAGE:figures/full_fig_p008_3.png] view at source ↗

**Figure 4.** Figure 4: A case study for PoT, comparing SFT and RE-SFT. [PITH_FULL_IMAGE:figures/full_fig_p013_4.png] view at source ↗

**Figure 5.** Figure 5: Instruction for DP data in TableBench. You are a table analyst. Your task is to answer questions based on the table content. The answer should follow the format below: [Answer Format] Final Answer: AnswerName1, AnswerName2... Ensure the final answer format is the last output line and can only be in the "Final Answer: AnswerName1, AnswerName2..." form, no other form. Ensure the "AnswerName" is a number or e… view at source ↗

**Figure 6.** Figure 6: Instruction for TCoT data in TableBench (along with WikiTQ and WikiSQL in our [PITH_FULL_IMAGE:figures/full_fig_p014_6.png] view at source ↗

**Figure 7.** Figure 7: Instruction for SCoT data in TableBench. [PITH_FULL_IMAGE:figures/full_fig_p015_7.png] view at source ↗

**Figure 8.** Figure 8: Instruction for PoT data in TableBench. 15 [PITH_FULL_IMAGE:figures/full_fig_p015_8.png] view at source ↗

**Figure 9.** Figure 9: Instructions for DeepSeek-R1 inserting Table Regions in CoT. [PITH_FULL_IMAGE:figures/full_fig_p016_9.png] view at source ↗

read the original abstract

Tables present unique challenges for language models due to their structured row-column interactions, necessitating specialized approaches for effective comprehension. While large language models (LLMs) have demonstrated potential in table reasoning through prompting and techniques like chain-of-thought (CoT) and program-of-thought (PoT), optimizing their performance for table question answering remains underexplored. In this paper, we introduce region-based Table-R1, a novel reinforcement learning approach that enhances LLM table understanding by integrating region evidence into reasoning steps. Our method employs Region-Enhanced Supervised Fine-Tuning (RE-SFT) to guide models in identifying relevant table regions before generating answers, incorporating textual, symbolic, and program-based reasoning. Additionally, Table-Aware Group Relative Policy Optimization (TARPO) introduces a mixed reward system to dynamically balance region accuracy and answer correctness, with decaying region rewards and consistency penalties to align reasoning steps. Experiments show that Table-R1 achieves an average performance improvement of 14.36 points across multiple base models on three benchmark datasets, even outperforming baseline models with ten times the parameters, while TARPO reduces response token consumption by 67.5% compared to GRPO, significantly advancing LLM capabilities in efficient tabular reasoning.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

Table-R1 layers region labeling in SFT with a mixed-reward RL step and reports clear gains on table QA plus big token savings, but the RL piece needs ablations to show it adds much beyond the supervision.

read the letter

The main thing to know is that this paper takes standard RL-for-reasoning ideas and applies them to tables by first training the model to flag relevant regions, then using a custom reward that mixes region accuracy with final answer quality while decaying the region signal and penalizing inconsistent steps. The reported outcome is an average 14-point lift across base models on three benchmarks, sometimes beating much larger models, along with a 67% drop in response tokens versus plain GRPO. That combination of region focus and efficiency is the practical hook. What the work does well is spell out the two new pieces clearly: RE-SFT that adds region evidence during supervised tuning, and TARPO that balances the two reward terms dynamically. The efficiency claim is especially useful because shorter outputs matter for real table tasks. The experiments cover multiple base models and datasets, which gives the results some breadth. The soft spot is the missing separation between the two stages. RE-SFT already supplies explicit region labels before RL starts, so the headline gains could come mostly from that extra supervision rather than from TARPO's decaying rewards and consistency penalties. If the paper shows only small further improvement when TARPO is added on top of RE-SFT, the central story shifts. I also want to see error bars, more baseline controls, and checks that the gains hold on held-out table formats. This paper is for groups working on structured-data reasoning in LLMs, especially anyone who already uses CoT or PoT and wants a targeted RL extension. A reader who cares about practical table QA or token-efficient inference will find concrete numbers to consider. It deserves peer review. The core adaptation is straightforward and the efficiency result is worth checking in detail, even if the experimental section needs tightening on ablations.

Referee Report

3 major / 2 minor

Summary. The paper introduces Table-R1, a region-based reinforcement learning method for table understanding in LLMs. It combines Region-Enhanced Supervised Fine-Tuning (RE-SFT) to identify relevant table regions with textual/symbolic/program reasoning, and Table-Aware Group Relative Policy Optimization (TARPO) that uses a mixed reward balancing region accuracy and answer correctness via decaying region rewards and consistency penalties. Experiments on three benchmarks report an average 14.36-point gain across base models (outperforming 10x larger baselines) and a 67.5% reduction in response tokens versus GRPO.

Significance. If the central performance and efficiency claims hold after proper controls, the work would offer a concrete advance in efficient tabular reasoning by showing how explicit region guidance plus tailored RL rewards can improve both accuracy and token efficiency over standard fine-tuning and GRPO baselines.

major comments (3)

[Experiments] Experiments section (performance tables and ablation discussion): the headline 14.36-point average improvement and 67.5% token reduction are attributed to the full Table-R1 pipeline, yet no ablation isolating RE-SFT alone versus RE-SFT+TARPO is reported. This leaves open whether the mixed-reward RL step (decaying region rewards + consistency penalties) is load-bearing or whether gains derive primarily from the additional region supervision in RE-SFT.
[Experiments] Experiments section (results tables): reported improvements lack error bars, standard deviations across multiple seeds, or statistical significance tests. Given the claim of outperforming models with ten times the parameters, these controls are necessary to establish that the observed deltas are robust rather than within run-to-run variance.
[Method (TARPO)] TARPO reward formulation (section describing the mixed reward and decay schedule): the region reward decay schedule and consistency penalty coefficient are treated as free parameters without sensitivity analysis or justification for the chosen values. This weakens the claim that the reward design reliably balances region accuracy and answer correctness across base models.

minor comments (2)

[Figures/Tables] Figure captions and table headers could more explicitly distinguish RE-SFT-only runs from full Table-R1 runs to aid reader interpretation of the ablation gap.
[Introduction] The abstract and introduction would benefit from a brief comparison to prior region-aware or structured-reasoning RL methods in NLP to better situate the novelty of TARPO.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for their constructive and detailed feedback. We have carefully addressed each major comment and revised the manuscript to incorporate additional experiments, statistical controls, and analyses as outlined below. These changes strengthen the presentation of our results without altering the core claims.

read point-by-point responses

Referee: [Experiments] Experiments section (performance tables and ablation discussion): the headline 14.36-point average improvement and 67.5% token reduction are attributed to the full Table-R1 pipeline, yet no ablation isolating RE-SFT alone versus RE-SFT+TARPO is reported. This leaves open whether the mixed-reward RL step (decaying region rewards + consistency penalties) is load-bearing or whether gains derive primarily from the additional region supervision in RE-SFT.

Authors: We agree that isolating the contribution of TARPO beyond RE-SFT is important for clarifying the source of gains. In the revised manuscript we have added a dedicated ablation study (new Table 4 and expanded discussion in Section 4.3) that directly compares RE-SFT alone against the complete Table-R1 pipeline. The results confirm that TARPO delivers further consistent improvements on top of RE-SFT, indicating that the mixed-reward RL component is load-bearing for both accuracy and token efficiency. revision: yes
Referee: [Experiments] Experiments section (results tables): reported improvements lack error bars, standard deviations across multiple seeds, or statistical significance tests. Given the claim of outperforming models with ten times the parameters, these controls are necessary to establish that the observed deltas are robust rather than within run-to-run variance.

Authors: We acknowledge the value of statistical robustness. The revised manuscript now reports standard deviations computed over five independent runs with different random seeds for all main results. Error bars have been added to the performance tables, and we include paired t-test p-values comparing Table-R1 against the strongest baselines. The reported gains remain statistically significant (p < 0.05) in the large majority of settings, supporting the reliability of the improvements. revision: yes
Referee: [Method (TARPO)] TARPO reward formulation (section describing the mixed reward and decay schedule): the region reward decay schedule and consistency penalty coefficient are treated as free parameters without sensitivity analysis or justification for the chosen values. This weakens the claim that the reward design reliably balances region accuracy and answer correctness across base models.

Authors: We agree that explicit justification and sensitivity analysis would strengthen the method section. The revised manuscript includes a new sensitivity study (Appendix C) that varies the decay schedule (starting epoch and decay factor) and consistency penalty coefficient across plausible ranges. Performance remains stable for values near the chosen settings, and we have added a short paragraph in Section 3.2 explaining the selection rationale based on preliminary validation runs. These additions demonstrate that the reward design is robust across the evaluated base models. revision: yes

Circularity Check

0 steps flagged

No circularity: method applies RL to tables with independent experimental validation

full rationale

The paper presents RE-SFT for region identification and TARPO for mixed-reward RL as explicitly engineered components on top of standard supervised fine-tuning and group-relative policy optimization. Performance gains are reported via benchmark experiments rather than any derivation that reduces by construction to fitted inputs, self-defined quantities, or load-bearing self-citations. No equations equate a claimed prediction to its own training signal, and the central claims rest on external test-set results rather than internal re-labeling of the same data.

Axiom & Free-Parameter Ledger

2 free parameters · 1 axioms · 0 invented entities

The approach rests on standard RL assumptions plus several tunable reward components whose exact values are not detailed in the abstract.

free parameters (2)

region reward decay schedule
Controls how region accuracy rewards decrease during training in TARPO.
consistency penalty coefficient
Weight applied to keep reasoning steps aligned with region and answer rewards.

axioms (1)

domain assumption Reinforcement learning with mixed rewards can effectively align LLM outputs on structured inputs when region evidence is provided.
Invoked to justify the effectiveness of TARPO and RE-SFT.

pith-pipeline@v0.9.0 · 5779 in / 1213 out tokens · 46970 ms · 2026-05-22T14:19:50.922713+00:00 · methodology

discussion (0)

Forward citations

Cited by 2 Pith papers

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V-tableR1: Process-Supervised Multimodal Table Reasoning with Critic-Guided Policy Optimization
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V-tableR1 uses a critic VLM for dense step-level feedback and a new PGPO algorithm to shift multimodal table reasoning from pattern matching to verifiable logical steps, achieving SOTA accuracy with a 4B open-source model.
Towards Robust Real-World Spreadsheet Understanding with Multi-Agent Multi-Format Reasoning
cs.CL 2026-04 unverdicted novelty 6.0

SpreadsheetAgent uses incremental multi-format reading, structural sketching, and verification to raise spreadsheet benchmark accuracy from 35.27% to 38.16%.

Reference graph

Works this paper leans on

17 extracted references · 17 canonical work pages · cited by 2 Pith papers

[2]

Extract the row corresponding to Guangdong

work page
[3]

Identify the value in the '2008' column for Guangdong

work page 2008
[4]

Final Answer: {rank}\

Determine the ranking based on the extracted value. Let's write the Python code to achieve this: ```python import pandas as pd # Load the data df = pd.read_csv('table.csv’) # Extract the row for Guangdong guangdong_row = df[df['year'] == 'guangdong’] # Get the value in the '2008' column ranking_2008 = guangdong_row['2008'].values[0] # Since the ranking is...

work page 2008
[5]

Load the data from the CSV file

work page
[6]

columns\

Filter the data to get the row corresponding to Guangdong. Up to this point, we can identify the table area needed to answer the question: <gold_area>{\"columns\": [\"year\", \"2010\", \"2009\", \"2008\", \"2005\", \"2000\"], \"rows\": [6], \"data\": [[\"guangdong\", 7, 7, 6, 5, 5]]}</gold_area>

work page 2010
[7]

Extract the value for the year 2008

work page 2008
[8]

Final Answer: AnswerName1, AnswerName2

Determine the ranking based on the value. Let's write the Python code to perform this analysis. ```python import pandas as pd # Load the data\ndf = pd.read_csv('table.csv’) # Filter the data to get the row for Guangdong guangdong_data = df[df['year'] == 'guangdong’] # Extract the value for the year 2008 ranking_2008 = guangdong_data['2008'].values[0] # De...

work page 2008
[9]

based on the question, write out your analytical approach, and then write Python code according to this approach

work page
[10]

The code needs to be concise and easy to understand, and if necessary, add comments for clarification

work page
[11]

Code blocks need to strictly start with ```python and end with ```

work page
[12]

If the user's question is not related to data analysis, please pol itely refuse

Your analysis must be based entirely on the above data. If the user's question is not related to data analysis, please pol itely refuse

work page
[13]

If there are results to be presented, please use the print function; if there are ch arts, please use the matplotlib library to draw them

You need to generate executable code. If there are results to be presented, please use the print function; if there are ch arts, please use the matplotlib library to draw them

work page
[14]

Final Answer: AnswerName1, AnswerName2

Ensure to load the table with command ```df = pd.read_csv('table.csv')```\n\n\nThe answer should follow the format below: [Answer Format] Final Answer: AnswerName1, AnswerName2... Ensure the final answer format is the last output line and can only be in the "Final Answer: AnswerName1, AnswerName2..." form, no other form. Ensure the "AnswerName" is a numbe...

work page 1990
[15]

Analyze the reasoning process

work page
[16]

Identify the rows and columns in the table that are indeed used to answer the question in the reasoning process

work page
[17]

After obtaining th e gold area, be sure to verify its correctness using the table area actually employed in the reasoning process that follows

In the reasoning process, once the table area is inferred, insert the formatted table area result here. After obtaining th e gold area, be sure to verify its correctness using the table area actually employed in the reasoning process that follows

work page
[18]

Modified Reasoning:

Return the modified CoT process, strictly follow the output format: Starting with "Modified Reasoning:", and output the original Reasoning process only with the gold area at an appropriate position. Do not modify any other parts of the original Reasoning process and do not output any extra characters. **Example Output Format: Modified Reasoning: To answer...

work page 1986

[1] [2]

Extract the row corresponding to Guangdong

work page

[2] [3]

Identify the value in the '2008' column for Guangdong

work page 2008

[3] [4]

Final Answer: {rank}\

Determine the ranking based on the extracted value. Let's write the Python code to achieve this: ```python import pandas as pd # Load the data df = pd.read_csv('table.csv’) # Extract the row for Guangdong guangdong_row = df[df['year'] == 'guangdong’] # Get the value in the '2008' column ranking_2008 = guangdong_row['2008'].values[0] # Since the ranking is...

work page 2008

[4] [5]

Load the data from the CSV file

work page

[5] [6]

columns\

Filter the data to get the row corresponding to Guangdong. Up to this point, we can identify the table area needed to answer the question: <gold_area>{\"columns\": [\"year\", \"2010\", \"2009\", \"2008\", \"2005\", \"2000\"], \"rows\": [6], \"data\": [[\"guangdong\", 7, 7, 6, 5, 5]]}</gold_area>

work page 2010

[6] [7]

Extract the value for the year 2008

work page 2008

[7] [8]

Final Answer: AnswerName1, AnswerName2

Determine the ranking based on the value. Let's write the Python code to perform this analysis. ```python import pandas as pd # Load the data\ndf = pd.read_csv('table.csv’) # Filter the data to get the row for Guangdong guangdong_data = df[df['year'] == 'guangdong’] # Extract the value for the year 2008 ranking_2008 = guangdong_data['2008'].values[0] # De...

work page 2008

[8] [9]

based on the question, write out your analytical approach, and then write Python code according to this approach

work page

[9] [10]

The code needs to be concise and easy to understand, and if necessary, add comments for clarification

work page

[10] [11]

Code blocks need to strictly start with ```python and end with ```

work page

[11] [12]

If the user's question is not related to data analysis, please pol itely refuse

Your analysis must be based entirely on the above data. If the user's question is not related to data analysis, please pol itely refuse

work page

[12] [13]

If there are results to be presented, please use the print function; if there are ch arts, please use the matplotlib library to draw them

You need to generate executable code. If there are results to be presented, please use the print function; if there are ch arts, please use the matplotlib library to draw them

work page

[13] [14]

Final Answer: AnswerName1, AnswerName2

Ensure to load the table with command ```df = pd.read_csv('table.csv')```\n\n\nThe answer should follow the format below: [Answer Format] Final Answer: AnswerName1, AnswerName2... Ensure the final answer format is the last output line and can only be in the "Final Answer: AnswerName1, AnswerName2..." form, no other form. Ensure the "AnswerName" is a numbe...

work page 1990

[14] [15]

Analyze the reasoning process

work page

[15] [16]

Identify the rows and columns in the table that are indeed used to answer the question in the reasoning process

work page

[16] [17]

After obtaining th e gold area, be sure to verify its correctness using the table area actually employed in the reasoning process that follows

In the reasoning process, once the table area is inferred, insert the formatted table area result here. After obtaining th e gold area, be sure to verify its correctness using the table area actually employed in the reasoning process that follows

work page

[17] [18]

Modified Reasoning:

Return the modified CoT process, strictly follow the output format: Starting with "Modified Reasoning:", and output the original Reasoning process only with the gold area at an appropriate position. Do not modify any other parts of the original Reasoning process and do not output any extra characters. **Example Output Format: Modified Reasoning: To answer...

work page 1986