Multimodal Tabular Reasoning with Privileged Structured Information
Authors: Jun-Peng Jiang
Yu Xia
Hai-Long Sun
Shiyin Lu
Qing-Guo Chen
Weihua Luo
Kaifu Zhang
De-Chuan Zhan
Han-Jia Ye
Venue: NeurIPS 2025 poster ()
Keywords: Tabular Reasoning; Multimodal Large Language Models; Multimodal Tabular Reasoning;
Primary area: deep_learning

Abstract: Tabular reasoning requires complex, multi-step information extraction and logical inference, such as aggregation, comparison, or calculation over tabular data. While recent advances have leveraged large language models (LLMs) for reasoning over structured text tables, such high-quality textual representations are often unavailable in real-world settings, where tables typically appear as images. In this paper, we tackle the task of tabular reasoning directly from table images. Our core strategy is to leverage privileged structured information---specifically, the ground-truth structured table data available during training but inaccessible at test time---to enhance multimodal large language models (MLLMs). The key challenges lie in: accurately aligning visual representations with the structured information, particularly mapping the visual evidence to logical steps; and effectively transferring the reasoning skills learned during training to the MLLM for visual inference. 
To address these, we introduce {\sc Turbo} (TabUlar Reasoning with Bridged infOrmation), a new framework for multimodal tabular reasoning using privileged information. {\sc Turbo} benefits from a structure-aware reasoning trace generator based on DeepSeek-R1, which contributes to high-quality modality-bridged information. On this basis, {\sc Turbo} repeatedly generates and selects advantageous reasoning traces, further enhancing the model's tabular reasoning ability. Experimental results demonstrate that, with limited (9k) data, {\sc Turbo} achieves state-of-the-art performance ($+7.2\%$ vs. previous SOTA) across multiple datasets.

Multimodal Tabular Reasoning with Privileged Structured Information
Jun-Peng Jiang 12,3+
Yu Xia
Hai-Long Sun 1.2.3+ Shiyin Lu
Qing-Guo Chen
Weihua Luo
Kaifu Zhang De-Chuan Zhan
Han-Jia Yet
School of Artificial Intelligence, Nanjing University, China
2 National Key Laboratory for Novel Software Technology. Nanjing University, China
Al Business, Alibaba Group.
[jiangip, sunhl, zhandc, yehj) @ laada.nju.edd.cn (daxiao.xy, running.lsy gingguo.cqg, weihua.luowh, kaifu.zkf? @ alibaab--inc.com
Abstract
Tabular reasoming requires complex, multi-step information extraction and logical inference, such as aggregation, comparison, calculation over tabular data. While recent advances have leveraged large language models (LLMs) for reasoning over structured text tables, such high-quality textual representations are often unavailable in
settings, where tables typically appear as images. this paper, we tackle the task of tabular reasoning directly from table images. Our core strategy is to leverage privileged structured tured table data available during training but inaccessible at test time-to enhance multimodal large language models (MLLMs). The key challenges lic in: accu- rately aligning visual representations with the structured information, particularly mapping the visual evidence to logical steps; and effectively transferring the rea- soning skills learned during training to the MLLM for visual inference. To address these, we introduce TURBO (TabUlar Reasoning with Bridged infOrmation), a new framework for multimodal tabular reasoning using privileged information. TURBO benefits from a structure-awant reasoning trace generator based on DeepSeek-R1, which contributes to high-quality modality-bridgee information. On this basis, TURBO repeatedly generates and selects advantageous reasoning traces, further enhancing the model's tabular reasoning ability. Experimental results demonstrate that, with limited (9k) data, TURBO achieves state-of-the- performance (+7.2% vs. previous SOTA) across multiple datasets.
the ground-truth strue-
Introduction
Tabular understanding focuses the extraction of information from various forms of tables, such as
structured tables and table images [16 31 64 73). Going a step further, tabular reasoning [91 77 89]
involves multi-step extraction, integration, and logical inference over tables derive the final answer, demonstrating its potential in fields such as finance [95], healthcare
(63 and scientific research (24).
75 particularly their increasing reasoning capabilities [48 55 21 74), performing tabular reasoning tasks becomes feasible. Current efforts primarily focus structured tables, where the input is typically the table's text structured representations such as Markdown [85 77 [8]. contrast, real-world scenarios, we can only have access to table images screenshots rather than clean, structured tables [50) 90 40 Given the gap
With the advancement of large language models (LLMs)
18
'Work done during an internship at Business. Alibaba Group Cooressponing author, email: yehj @ lamda.nju.edu.co
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

---

between training imputs and deployment conditions, a natural question arises: can structured tables be leveraged as privileged information [72 7TV to improve the reasoning capabilities of MLLMs?
Structured tables inherently contain rich information, allowing for fast and precise extraction of the content in each row and column (26] 76.. However, they often struggle to capture the full structural context, particularly in cases where the layout and relationships between rows and columns are complex [40]. On the other hand, while table images provide clear visual cues, current MLLMs still face challenges in effectively extracting and integrating corresponding information, particularly for complex tasks that require multi-step understanding
80]
Beyond the gap between structured tables and images, while recent MLLMs have shown progress in domains such as mathematical reasoning [62 [66]. their performance on tabular reasoning tasks remains limited [65] 92.. In our setting, the model is required to extract relevant content from table images, integrate information based on row and column relationships, and then perform logical inference or numerical computation to arrive at the correct answer. This multi-step process places high demands on both perception and reasoning, exposing clear limitations in current models.
Therefore, to enhance multimodal tabular reasoning capability with privileged structured tables, there are two main challenges. Given the complexity of structured tables, not all content is equally relevant to a given question. is therefore crucial to identify and extract the most relevant information to guide model learning. Furthermore, once relevant content is identified, the next challenge is to effectively leverage it to improve the reasoning ability of MLLMs, enabling them to progressively construct accurate, multi-step reasoning paths.
To address the challenges, we propose TabUlar Reasoning with Bridged infOrmation (TURBO), framework that integrates structured table information during training to enhance multimodal tabular reasoning. Our approach leverages modality- reasoning traces as bridged information, facilitating the knowledge transfer from LLMs to MLLMs. Additionally, we employ reinforcement learning techniques to further enhance the model's reasoning capabilities, allowing it to progressively improve its performance complex tabular reasoning tasks.
Specifically, we construct a new pipeline tailored for tabular reasoning. We start by feeding structured markdown tables and their corresponding QA pairs into DeepSeek-RI [2I]. the SOTA reasoning LLM, to generate structure-aware chains of thought to bridge structured tables and images. This process yields high-quality reasoning data in the form of [question, reasoning, answer] triples, which effectively filter out redundant content and emphasize question-relevan reasoning traces. We then conduct supervised fine-tuning (SFT), equipping the MLLM with initial tabular reasoning capabilities. Building on this foundation, by iteratively sampling and selecting the relative advantages in reasoning paths as GRPO method [6], we further enhance the reasoning capability, progressively strengthening the reasoning performance. Experimental results demonstrate that our TURBO achieves significant improvements (+7.2% over baselines) with limited data, showing high generalization ability and interpretability. Our contributions are threcfold:
We highlight the practical value of using structured tables as privileged information during training to enhance reasoning over table images, as such structured tables are often unavailable at inference time in real-world scenarios.
We propose a new framework that utilizes privileged structured information to bridge the modality gap and enhance the tabular reasoning capabilities of MLLMs, combining structure-aww reasoning trace generation with iterative optimization.
Experimental results show that method achieves substantial performance improvements over datasets with limited training data. Furthermore, the resulting model demonstrates strong robustness and interpretability in its reasoning process.
2
Related Work
2.1 Tabular Reasoning
Tabular data is of great importance due to its broad applicability in real-world domains, encompassing a variety of learning tasks from standard classification and regression tasks [82
38
84
28
to
open-world challenges 22 6 30
Besides, tabular reasoning is part of tabular understanding, which involves comprehending the information contained within a table and can be
27
39
83
5
broken down into several tasks, such as Table Structure Recognition (TSR) [59 (88. Table Detection

---

(TD)
19
35
and Table Question Answering (TQA)
69 31 Traditional methods, whether 9
90 have made significant strides in recognizing the structure and content of tables. However, we focus on more challenging tabular reasoning tasks.
OCR-based 2 13 20 or OCR-free 46
32
73
Tabular reasoning involves multi-step information extraction and logical inference tabular data. In the field of tabular reasoning, several recent works have made notable progress. OPENRT [91] is open-source framework designed for reasoning over tabular data. It enables the reproduction of existing table pre-training models for fair performance comparison and supports rapid development of new models. [1] explores different table representations and directly prompts LLMs and MLLMs, investigating how structural formats influence reasoning capabilities, Chain of Table 777 employs in-context learning to iteratively generate operations and update the table, allowing LLMs to construct reasoning chains where each step builds upon the previous output. HIPPO 40] represents tables using both textual and visual modalities, and optimizes MLLLMs to learn richer and more comprehensive information with DPO [57]. However, these works primarily focus on reasoning over structured tables and achieve only limited performance. Since structured tables are rarely available in real- wollw settings, reasoning over table images becomes more practical choice.
2.2 MLLMSs for Reasoning
The field of multimodal large language models (MLLMs) has advanced significantly, particularly in integrating visual and textual processing. Modern MLLMs combine visual encoders [56] 68 87 25
671. LLMs, and fusion modules. Models like BLIP-2 to bridge vision encoders and frozen LLMs. MiniGPT-4 [93] combines Q-Former with a linear
34]] and InstructBLIP [14 use Q-Former
projector for better alignment between vision and LLMs. LLaVA (37) employs an MLP projector to improve vision-LL.M LLM alignment. Alongside these open-source advancements, proprictary models like have excelled in benchmarks
GPTT44//0 47 1, Gerini2.5pro [70]. and Swen2.5-VLL-PPus/MM
and woal-world applications.
Recent developments in MLLMs have led to significant improvements in reasoning tasks involving both textual and multimodal data [48, 21 [4, 666 [3 43). Most current approaches rely on Chain- of-Thought (CoT) techniques [78 to train MLLMs for sequential reasoning. Notable data-driven initiatives include Math-LLaVA (62) which introduced the Math V360K dataset, and MAmmoTH.
VL [22) which builds large multimodal CoT datasets scale. Another research direction focuses on improving vision-text aligmment. MAVIS [88] fine-tunes a math-specific vision encoder with a curated set of captions, while Math-PUMA [96] aligns modalities by utilizing the KL divergence in next-token prediction distributions. In our paper, we focus on reasoning over table images, where structural information is not explicitly available and must be inferred through visual cues and multi-step reasoning to reach the correct answer.
3 Preliminaries
In this section, first introduce the notation of multimodal tabular reasoning with privileged structured information task, followed by some preliminary experiments and analyses.
3.1 Multimodal Tabular Reasoning with Privileged Structured Information
Tabular reasoning refers to the task of answering questions that require understanding and logical inference over tabular data. This often involves multi-step reasoning, including locating relevant cells, integrating information across rows and columns, and performing operations such as comparison, aggregation, or arithmetic.
In real-world applications, high-quality structured tables T, such as markdown tables, are often difficult obtain. Instead, what is more readily available are table images V. such as screenshots or scanned documents. These image- based tables present significant challenges for reasoning, as the model must perceive both the content and the visual layout before performing any logical inference.
To address this, our goal to enhance multimodal large language models M to generate a response X, for the tabular reasoning question Q over table images V by leveraging structured tables T as privileged information during training. While structured tables are not accessible at inference time, they offer rich semantics and precise structure that can guide the model during training. By
3

---

ACC
QwenZ.-VV.
1.00
InternVL2.5
wio ST
Commute time (minutes]:
Quoiiee: A Almino- cragaste sareaje rasles
Oviz
wio ST
Leaf
Aoed thet countate linos Blow saag sveasako snnskoff
0
0.50
01277
StaneYee
25799
Dv02 Saaed th tabe, the aasoer
Termeneiiins krower
0.00
TABMWP
Figure 2: Challenge of current MLLMs on tabu- lar reasoning. This example illustrates the limita- tions of current MLLMs in performing multi- -teli reasoning and numerical computation. The task requires sequential reasoning based on the image, but existing models often lack such capabilities or make errors during intermediate steps, ultimately leading incorrect answers.
Figure 1:
Impact of structured tables (ST).
We evaluate the effect of providing struc- tured tables or not as input across Qwen2.5- VL 3. Intern V1.2.5 I1I and Ovis2 A2 on the TABMWP 41 and WTQ (22 datasets. The re- sults show consistent performance drop when structured tables are removed, highlighting their importance for effective tabular reasoning.
combining visual table inputs with structured supervision, we aim to improve the MLLM's ability to perform accurate and interpretable reasoning over table images. Our goal is for the final answer, X., to be as consistent as possible with the correct answer Ya.
min Σ ((M M
(1)
é is the loss function that measures the discrepancy between prediction and ground-truth. "I" indicates that the answer is generated conditioned on the corresponding structured table T. Our objective is to enhance the M's reasoning capability with the privileged structured information. In situations where structured tables is not available in real-world scenarios, we expect M can still provide accurate answers given only the table image V. Therefore, during the inference phase, we measure the performance of M based on its prediction accuracy given any test image.
3.2
Analysis on MILMs in Tabular Reasoning
To conduct our preliminary study, select several of the most advanced and widely adopted open- source MLLMs, including Qwen2.5-VL. [3. InterVVL2.5 [1]. and Ovis2 [42], and evaluate them on TABMWP 41 and WikiTableQuestions (WTQ) (52). We randomly sample 100 examples from each dataset for testing. Since tabular reasoning tasks involve open-ended question answering, it is often challenging to directly judge the correctness of model outputs. We employ Swren255-7BBInssteee as an external evaluator. Specifically, we provide the LLM with the question, ground-truth answer, and the model's prediction. By converting the evaluation into single-modality setting, this approach reduces ambiguity and improves the reliability of the evaluation, offering a more accurate assessment of MLLMs' reasoning capabilities.
(79)
To investigate the role of structured tables in tabular reasoning, we conduct preliminary experiments using MLLMs. Specifically, compare the model's performance when reasoning is performed with access to structured tables versus relying solely on table images during inference.
Structured Information Matters in Tabular Reasoning, Figure I presents comparison between inference results with and without access to structured tables. We observe a significant drop in performance across all models when structured tables not provided. This highlights the difficulty MLLMs face when relying solely on table images, as they must implicitly recover the table's structure and relevant relationships before performing reasoning. In contrast, structured tables offer clean and explicit content, which greatly facilitates accurate information extraction. These results underscore the importance of leveraging structured representations to guide model reasoning, even if they only available during training.
MLLMss Lack Explicit Reasoning Traces. Both WTQ and TABMWP contain complex, reasoning- intensive questions that require multi-step inference and arithmetic operations reach the correct. answer, as in the case in Figure 2 However, our analysis reveals that current MLLMs often fall

---

Data Generation Pipeline
Question: What is the total number of wins
Reasoning Trace:
Structured Table
First, let's out golfers from United
listed for the United States?
States. Looking the "Country" column:
Golfer
Country
Mins
Tiger Woods: United States, 18 wins.
Hunter Maham: United States, wins.
Tiger
Phil Mickelson: United States, 2 wins.
Geoff Ogilvy
Australio
Let me calculate that, 18 plus 2 is 20, plus another
Darren Clarke
Northern Ireland
ELs
Hunter Mohan
United States
Let me check again: So the final answer is 22.
Phit Micketson
United States
Poulter
Eng
reject sampling
Answer: 22
Training Flow
Table Image
Inll
generate group of responses
reward
Rack
think I, answer
Tause
Ledktt
An
think 2, answer 2
SFT
Smea
relative
17 10taae
Tiak
think answer
advantage
Figure 3: Data generation pipline and training flow of our TURBO framework. We first input the structured table, question, and answer into DeepSeek to generate a reasoning trace, and apply reject sampling improve data quality. Then, supervised fine-tuning (SFT) is performed the collected data for privileged information alignment. Finally, we generate a group of answers for each question and compute their relative advantages based on their reward further reinforce the reasoning ability.
short in these scenarios. Despite having access table images, these models tend to produce final answers directly, frequently bypassing any clear intermediate reasoning steps. In the absence of explicit prompting for chain-of-thought generation, their outputs rarely exhibit clear reasoning traces. This lack of step-by-step thinking not only undermines interpretability but also frequently results in incorrect predictions. Furthermore, even Qwen shows potential in reasoning traces, there are critical intermediate mistakes, leading to the final wrong answer. These observations highlight the limitations of current MLLMs in tabular reasoning and underscore the need for methods that can explicitly leverage structured reasoning capability and align it with visual representations.
4 Multimodal Tabular Reasoning with Bridged Information
In this section, we introduce our TURBO framework (as shown in Figure 3 of multimodal tabular reasoning with privileged structured information through structure-aware reasoning trace generator. Then we sample and select the advantageous reasoning paths, further enhancing the reasoning ability.
4.1 Bridging Structured Tables and Images
central challenge multimodal tabular reasoning arises from the intrinsic modality gap between structured tables and table images. These two modalities, though semantically aligned, are processed very differently by MLLMs, leading to differences their reasoning behavior.
Structured tables, such as those in Markdown or HTML, explicitly encode row-column relationships, enabling accurate and fine-grained access to cell contents 7 (2). This makes them particularly suitable for tasks that require semantic-leve reasoning. However, they often fail to capture complex layout nuances such as merged cells, multi-level headers, or visual hierarchies, which are common in real-world tables [5] 40 In contrast, table images naturally preserve these structural cues through visual patterns like borders, spacing, and alignment. Yet, due OCR errors, visual noise, or resolution limits, accurately extracting textual content from table images remains a significant challenge 44. This inherent modality gap highlights the complementary nature of structured tables and table images imch captures essential but distinct aspects of tabular information.
5

---

Unified Reasoning across Modalities. However, despite their representational differences, both structured tables and table images ultimately convey the same underlying information relevant to reasoning tasks. From a reasoning perspective, the inference trajectory-is -ie., the logical path leading from question to answer-would remain consistent across both modalities. correct reasoning chain, once established, can guide models to identify the relevant content and apply appropriate reasoning steps, regardless of whether the input is structured table or its visual counterpart. This highlights that the essence of tabular reasoning lies not in the input format but in the ability to extract and compose the necessary information into a coherent, accurate reasoning process.
Therefore, it is natural to leverage the strong reasoning capabilities of current large language models to extract high-quality reasoning chains for tabular data. These structured chains of thought are -idlaitt--ideeeeddent and can serve as valuable supervision signals for improving multimodal tabular reasoning. To generate structure-aware reasoning traces, we leverage the available structured tables during training. Specifically, for each markdown-formatted table T and its associated question- answer pair A), we input (T, Q, A) into DeepSeek-R1 (21). a state-of-the-an reasoning LLM. By providing the ground-truth answer A alongside the question and table, we guide the model to produce a coherent and accurate reasoning process R (2.,, ] that logically connects the question to the correct answer.
Reject Sampling over Reasoning. This approach produces high-quality supervision triplets (Q, R. A), where the reasoning trace R captures the key logical steps, relevant table elements, and necessary operations to arrive at the correct answer. To further improve data quality, we apply additional reject sampling: overly verbose or contradictory reasoning srees-often caused by in- consistencies between DeepSeek's 's generated answer and the provided ground-truth answer-are removed. We also employ an auxiliary strong LLM (e.g., Qwen2.5-72BB In72tutnst to assess the coherence and correctness of each trace, ensuring that only faithful and focused reasoning chains are retained. After reject sampling, we obtain approximately 9k high-quality examples that serve as reliable supervision for enhancing multimodal tabular reasoning.
In summary, we introduce the bridged information that successfully aligns structured tables with table images through reasoning chains. Our structure-wwar reasoning trace generator provides high-quality reasoning traces, facilitating the transfer of reasoning from structured inputs to images.
4.2 Enhancing MILMs with Reasoning Capability
Preliminary Exploration of Reasoning Capability. The most straightforward way to enhance MLLMs with reasoning ability is to supervised fine-tuning (SFT) on high-quality reasoning data. We construct the following system prompt to guide the model's learning process: You are helpful assistant. For each question, first think through your reasoning, then provide an answer Format your response as: <think>Your reasoning process/thhink>xxmseee>//
final answer</answer>, Each training ground truth is formatted as follows: <think> ht/th//kthhhiaaaaeeeerr A<t /answr>>> Through SFT, this process enables MILMS learn the "reasoning- first, answer-later" paradigm. Moreover, since the reasoning traces are -adaaity-invarriant, the reasoning ability can be preliminarily transferred from LLMs to MLLMs across modalities.
Reinforcing Multimodal Reasoning Capability in MLLMs. Inspired by [21], we adopt Group Relative Policy Optimization (GRPO) as the reinforcement learning algorithm to further enhance the reasoning capability. Unlike SFT, which minimizes token-level prediction errors, GRPO leverages policy gradients derived from reward signals. This enables the model to explore broader range of possible solutions, encouraging more diverse and complex reasoning behaviors [6]..]]
Specifically, we define questions as Q and the current policy as Toaia" For each question g E Q, group of responses (01, ....... OG) is generated from Te.ia" A fixed reference policy Fyef is also used to regularize training. The optimization objective for the updated policy ma is defined as:
(0) E. NTo
no (o: ④))
To (o: (q)
(2)
min
A,, clip
Tows (oz (g) 1 - e, ++ A: - -BDKI. (Tol(
Tous (o:1g)

---

Here, e is the clipping threshold, and 3 is the weight for the KL divergence penalty. The ad A; for each response o, computed relative the group as:
mean (fr1, (2,,,,,...,, A std (fri, rG)
(3)
This approach normalizes the reward across the group to obtain relative advantage signal. The KI. divergence term is defined as:
DKL. (Ta [ret) Tref (os ( no (o: ())
Tree (o: q) mo (o; 9)
log
1.
(4)
By leveraging relative advantage, the model able to perform self-comparison across its own set of generated responses, identifying which responses reasoning paths relatively more effective. Meanwhile, the KL divergence term acts as a regularizer, ensuring that the updated policy does not deviate excessively from the reference model in a single optimization step. This constraint helps maintain training stability by preventing abrupt policy shifts, enabling the model to improve progressively in controlled and stable manner.
Unlike standard PPO [60), GRPO removes the need for a critic network by estimating the relative advantage within each sampled group. This not only simplifies implementation but also significantly reduces computational overhead. In our tabular reasoning setting, the reward function is composed of two parts: format reward and accuracy reward.
The format reward encourages the model to adhere to structured reasoning format by using <think></thind and <answer></amd </aswer>>>>>>>> tags in its response. This design promotes a two- step process where the model first articulates a chain of reasoning based on its understanding of the table, and then explicitly states the final answer derived from that reasoning. By rewarding outputs that follow this format, we guide the model develop more interpretable and systematic reasoning behavior. The accuracy reward is rule-based and evaluates whether the content in the (oower><<answers tags matches the ground truth labels. This component directly incentivizes correctness in the final answer, ensuring that the reasoning process ultimately leads to accurate outcomes.
In summary, reward-guider GRPO efficiently enhances the model's reasoning ability in tabular tasks by selecting superior responses within group, enabling seff-improvement without the overhead of value function. Through this two-stage learning framework, our model develops a strong tabular reasoning capability, effectively bridging structured and unstructured modalities while enabling robust, interpretable reasoning across table images.
5 Experiments
In this section, we first present the experimental setup, including datasets, baselines, and imple- mentation details. We then demonstrate the effectiveness of our approach through comprehensive experiments and ablation studics. Finally, case studies further highlight the interpretability and robustness of our method in complex tabular reasoning scenarios.
5.1 Experimental Setup
Implementation Details. We conduct all experiments based on the Ovis2 [2] Our training pipeline consists of two stages: supervised fine-tuning (SFT) and reinforcement learning (RL.) During the SFT stage, we train our model using 4xA100 GPUs for hour with a batch size of 128 and a learning rate of 2c-6. This stage allows the model to learn structure-aware reasoning patterns from high-quality [question, reasoning, answer] triples. In the RL stage, we further enhance the model's reasoning ability by applying GRPO with 8x0100 GPUs for 24 hours. We use smaller learning rate of 5e-7 with the same batch size of 128. For each question, the model generates 16 candidate responses, which are then used to compute relative advantages and guide the optimization process.
Evaluation Benchmarks. Following HIPPO 40.
we evaluate the tabular reasoning capability of our model on a diverse set of publicly available benchmarks. Specifically, for Tabular Question Answering (TQA) tasks, we use five representative datasets: TABMWP 4T) WTQ [52] HiTab [12]
and TAT-QA (44. We export FeTaQA [45] due to its evaluation metric being BLEU [51), which
7

---

Table Accuracy comparison across tabular reasoning benchmarks. Our model, TURBO, consistently achieves the best overall performance, demonstrating strong reasoning ability and effective integration of privileged structured table information. Even when compared the strongest baseline Qwen2.5-
VI., TURBO achieves an average improvement of 7.2%.
Question Answering
Fact Verification TabFact
Method
MMMU
Average
TABMWP
WTQ
HiTab
TAT-QA
InfoTabs
Intern InterVL-22. 2L5
90.88
43.19
45.94
34,97
66.46
55,50
41.46
54.06
Qwen2.5-VI. [3
92.48
65.85
67.09
70.54
83.01
77.91
42.07
71.28
MiniCPM-V-2.6 81
83.68
47.97
56.53
51.55
78.48
73.03
31.10
60.33
HIPPO 4D
87.34
55.71
63.13
61.40
82.29
75.70
HIPPO w/o ST
85.83
49.10
57.23
56.22
80.20
72.74
35.98
62.47
Table-LLaVA
53.20
16.62
7.87
10.49
$7.62
66.78
17.68
32.89
TabPedia 990
10.66
23.53
6.54
13.08
35.49
2.43
2.44
13,45
Ovis2
92.00
58.76
68.59
47.67
80.80
74.11
48.17
67.16
Ovis2-CoT
92.12
60.80
66.43
48.70
81.61
72.46
50.61
67.53
TURBO
96.75
67.80
72.15
73.21
85.81
81.89
57.32
76.42
does not focus the accuracy of question answering. For Table Fact Verification (TFV) tasks, include TabFact [10] and InfoTabs [23]. To further assess the robustmess of our approach, we conduct additional experiments MMMU (86), evaluating our model all table-related questions within this challenging multimodal benchmark. This allows us to verify whether the reasoning capabilities learned through framework generalize unseen and diverse table images in complex real-world settings. For TQA, we evaluate model performance using Accuracy (ACC), and in TFV, we use the binary classification accuracy for TabFact (true/false outputs) and multi-class accuracy for InfoTabs cenail/contraaiictneutra outputs). We employ Swven.5--22BInmae Specifically, we provide the LLM with the question, ground-truth answer, and the model's prediction.
[79] as an external evaluator.
Comparison Methods. For comprehensive comparisons, we select leading open-source MLLMs and those in tabular understanding, including Qwen2.5-VL-7B [3] Intern VL-2.558B (II), MiniCPM- V-2.6-8B [81], Ovis2-8B [42. Table-LLaVA-7B 192, TabPedia-77 7B 190), and HIPPO-8B [40].
5.2
Main Results
In this experiment, evaluate the table reasoning effectiveness of our TURBO and baseline models on both the TQA, TFV tasks, and MMMU. For the HIPPO method, which leverages both structured tables and table images during training and inference, we report its performance under the original setting with structured tables and table images as input. Additionally, we include HIPPO without structured tables "HIPPO w/o ST", variant that takes only table images input, which better aligns with our setting and serves as fairer comparison for evaluating purely from table images.
To ensure fair comparison with HIPPO, we follow the same data sampling strategy by randomly selecting 10k examples in these training datasets as our training set, which do not overlap with our test set. Furthermore, evaluate the generalization capabilities of TURBO completely unseen data distributions and formats, we included the challenging MMMU benchmark as an additional test set. We then apply structure-awee reasoning trace generator to produce reasoning traces. To maintain high data quality, we further conduct reject sampling on incorrect or low-quality generations. As result, we retain approximately 9k high-quality training samples for SFT and RL for epoch. Results are in T
Across all datasets, model TURBO achieves the best overall performance, demonstrating clear and consistent improvements over existing baselines. Specifically, TURBo surpasses the strong tabular understanding baseline HIPPO up to relative improvement of 21.7% over TQA datasets (in WTQ), and by 8.2% over TFV datasets (in InfoTabs), Notably, compared to the variant HIPPO "w/o ST" (which only table images and is thus more comparable our real-wo real-world setting), average, our method achieves a 26.8% improvement on TQA and 9.8% on TFV, highlighting the effectiveness of our reasoning supervision and privileged structured information.
Furthermore, on the challenging MMMU benchmark, which evaluates table reasoning in real-world multimodal scenarios, TURBO reaches 57. .2.2, outperforming the previous best method Ovis2-CoT

---

90000
63.12
80.12
01.58 30.30
10.09
Accuracy (%)
70.00
72,31
23.22
76.13
66:18
61.30
05.00
(2:53
50.73
5732
51.66
40
TABMWP
WTQ
HiTab
TAT-QA
TabFact
InfoTabs
MMMU
Ovis
Ovis w/ SFT
Ovis w/ RL
Turbo
Figure 4: Ablation study of components in TURBO on each dataset. TURBO achieves consistent and significant improvements over the Ovis baseline on all datasets, demonstrating the effectiveness of each introduced component.
Year
Project
Question: Maxwell Software, Inc., following mutually exclusive projects. Suppose the company the NPV rule rank these projects, Which project should be chosen
0
-$20,000
$24,000
13.200
14.100
the appropriate discount rate percent?
8,300
9,300
A: Project A
3,200
7.600
Project B
Turbo: <think--Okay, let's sec. The problem about choosing between Project A and Project B using NPV with 15%
discount rate.
First, need to calculate the NPV for each project. Looking at the table, the initial investment for Project A is -$20,000, and for Project -$24,000, The cash flows for cach year. The NPV formula ... discounted by discount rate (15% 0.15).
For Project A/'nYear 0: -22,,000 (initial investment Year 813,200 (1+0.15))1 $13,200 1.15
NPV
$220,000 $11,478.26 $6,279.07 $2.104.33 = -S138.11 14
For Project B:inYear 0: $$2,,000 (initial investment)aal Year I: 1:514,100 /1011000/1111000 (1+0.15)*1 1 $14,100 1.15 = $12,260.87 NPV B
-$24,000 + $12,260.89 + $7,401.77 $4,997.45 $659.31
Comparing the NPVs, Project <answerBB<annswrr>
higher than Project A. Therefore, Project should be chosen
Figure 5: Case study of Turbo. Our method accurately extracts relevant information from the table image (highlighted in blue), formulates the appropriate calculation expressions (in pink), and performs step-by-ster reasoning to arrive at the correct final answer (in green). This demonstrates the effectiveness of TURBO in both visual grounding, mathematics, and logical reasoning.
by 13%, and surpassing HIPPO by 59.3% relative improvement, showcasing the robustness and generalization of our model under diverse and complex table formats.
These results validate that our two-stage learning framework not only equips MLLMs with strong reasoning capability, but also highlights its effectiveness in bridging the gap between structured tables and visual table inputs, enabling the model absorb and utilize privileged structural information.
5.3 Further Studies
Ablation Study. To better understand the contribution each component our two-stage framework, we conducted an ablation study on our baseline model. As shown in Figure E we compare the following variants: Bascline: The base model Ovis2 without reasoning supervision. "w/ SFT": Supervised Fine-tuned with our structure-aware reasoning traces. "w/ RL": Optimized with reward- driven reinforcement learning stage. TURBO: The full model trained with both SFT and RL stages.
From the results, we observe consistent improvements across all evaluation tasks. Compared to the baseline, adding SFT alone brings 10.1% relative improvement on average, demonstrating that dtructure-aware reasoning traces help the model better understand table semantics. The RL stage also introduces 11.8% performance gains, particularly on complex QA datasets like TAT-QA, showing that relative advantage can refine reasoning fidelity and answer consistency. Finally, the full TURBO model achieves the best performance across the board, 13.8% on average. This confirms that both stages in framework complementary and jointly crucial for maximizing reasoning ability.

---

Case Studies. To further demonstrate the effectiveness of our approach, we present a case in Figure that showcases the reasoning capability of our TURBO. In this example, the task involves interpreting a table image and answering a complex question that requires multiple-step reasoning, including visual grounding, mathematics, and logical reasoning. More cases and experiment results, such as more ablation studies about the extension of our framework, more experiments on inference time, can be found in Appendix B
In the baseline model, answers are generated directly without an interpretable reasoning process. After the SFT stage, the model begins to exhibit basic reasoning behaviors but still makes shortcuts, such as incorrect calculations. With RL tuning, TURBO chooses advantageous answers in group of responses, generates step-by-step reasoning, accurately locates key table elements, and produces interpretable answers, demonstrating clear improvements in both reasoning depth and accuracy.
6 Conclusion
In this paper, we propose TURBO, a new multimodal large language model tailored for real-world tabular reasoning tasks, where structured tables are used as privileged information. Our method leverages a two-stage training paradigm that integrates structured reasoning supervision into MLLMs. Specifically, we first introduce high-quality, structure-aware reasoning trace generation mechanism. enabling the model to internalize a thinn-tten--nse then further refine the reasoning capabilities via reinforcement learning, aligning the model's outputs with more accurate and interpretable reasoning behaviors. Our key contributions to novelty are: 1) a novel application of learning with privileged information; 2) the modality-bridging data heneration pipeline; 3) a synergistic and effective training framework. Extensive experiments show that TURBO consistently achieves state-of-the-art performance among open-source MILMs. We hope this work inspires future research into multimodal reasoning, especially in developing more systematic and interpretable frameworks for real-world tasks.
paradigm during supervised fine-tuning, We
Acknowledgments and Disclosure of Funding
This work is partially supported by National Key R&D Program of China (2024YEE0202800), NSFC (62376118), Collaborative Innovation Center of Novel Software Technology and Industrialization. Postgraduate "AI+" Research and Practical Innovation Project (KYCX25 0328).
References
[1] Josh Achiam, Steven Adler, Sandhini Agarwal, Lama Ahmad, Ilge Akkaya, Florencia Leoni Aleman, Diogo Almeida, Janko Altenschmidr, Sam Altman, Shyamal Anadkat, et al. Gpt-4 technical report. arXiv preprint arXiv:2303.087: 08774, 2023.
[2] Srikar Appalaraju, Bhavan Jasani, Bhargava Urala Kota, Yusheng Xie, and R Manmatha. Doc- former: End-to-end transformer for document understanding. In Proceedings of the IEEE/CVF international conference computer vision, pages 993-1003, 2021.
[3] Shuai Bai, Keqin Chen, Xuejing Liu, Jialin Wang, Wenbin Ge, Sibo Song, Kai Dang, Peng Wang, Shijie Wang, Jun Tang, et al. Qwen2. 5-vl technical report. arXiv preprint arXiv:2502.13922 2025.
[4] Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al. Language models are few-shot learners. Advances in neural information processing systems, 13:1877-1901, 2020.
[5] Hao-Run Cai and Han-Jia Ye. Feature-aware modulation for learning from temporal tabular data. In Advances in Neural Information Processing Systems 39, 2025.
[6] Hao-Run Cai and Han-Jia Ye. Understanding the limits of deep tabular methods with temporal shift. In Proceedings of the 42nd International Conference on Machine Learning, pages 6366-6386, 2025.
[7] Fay Chang, Jeffrey Dean, Sanjay Ghemawat, Wilson C Hsieh, Deborah A Wallach, Mike Burrows, Tushar Chandra, Andrew Fikes, and Robert E Gruber. Bigtable: A distributed storage system for structured data. ACM Transactions on Computer Systems (TOCS), 26(2):1-26, 2008
[8] Yupeng Chang. Xu Wang, Jindong Wang, Yuan Wu, Linyi Yang. Kaijie Zhu, Hao Chen, Xiaoyuan Yi, Cunxiang Wang, Yidong Wang, et al. A survey on evaluation of large language models. ACM transactions intelligent systems and technology, 15(3):1-45, 2024.
10

---

[9]
Wenhu Chen, Ming- Wei Chang, Eva Schlinger, William Wang, and William W Cohen. Open question answering over tables and text. arXiv preprint arXiv:2010. 10439, 2020.
[10] Wenhu Chen. Hongmin Wang. Jianshu Chen, Yunkai Zhang. Hong Wang, Shiyang Li, Xiyou Zhou, and William Yang Wang. Tabfact: A large-scale dataset for table- fact verification.
arXiv preprint Xrv: /909.02 024.002144440 2019.
[11] Zhe Chen, Weiyun Wang, Yue Cao, Yangzhou Liu, Zhangwei Gao, Erfei Cui, Jinguo Zhu, Shen- glong Ye, Hao Tian, Zhaoyang Liu, al. Expanding performance boundaries of source multimodal models with model, data, and test-time scaling. arXiv preprint 2oXiv:2422.03 2024.
[12] Zhoujun Cheng, Haoyu Dong, Zhiruo Wang, Ran Jia, Jiaqi Guo, Yan Gao, Shi Han, -uang Lou, and Dongmei Zhang. Hitab: A hierarchical table dataset for question answering and natural language generation. arXiv preprint arXiv:2 1et::2208..6722 12, 2021.
[13] Cheng Da, Peng Wang. and Cong Yao. Multi- Shatiiggaanllritty prediction with learnable fusion for scene text recognition. arXiv preprint arXiv:2307. 132444 2023.
[14] Wenliang Dai, Junnan Li, D Li, AMH Tiong, Zhao, W Wang, B Li, P Fung, and S Hoi. vision-language models with instruction tuning. arxiv 2023. arXiv preprint arXiv:2305.0 230.::3305006500, 2, 2023.
Instructblip: Towards general-purpont
[15] Naihao Deng. Zhenjie Sun, Ruiqi He, Aman Sikka, Yulong Chen, Lin Ma, Yue Zhang, and Rada Mihalcea. Tables as texts or images: Evaluating the table reasoning ability of Ilms and milms. In Findings of the Association for Computational Linguistics ACL 2024, pages 407-426, 2024.
[16] Xiang Deng, Huan Sun, Alyssa Lees, You Wu, and Cong Yu. Turl: Table understanding through representation learning. ACM Special Interest Group Management of Data Record,
51(1)333-40, 2022.
[17] Hao Feng, Zijian Wang. Jingqun Tang, Jinghui Lu, Wengang Zhou, Houqiang Li. and Can Huang. Unidoc: A universal large multimodal model for simultaneous text detection, recognition, spotting and understanding. arXiv preprint arXiv:2308. / /5592. 2023.
[18] Jiale Fu, Yuchu Jiang, Junkai Chen, Jiaming Fan, Xin Geng, and Xu Yang. Speculative ensemble: Fast large language model ensemble via speculation. arXiv preprint arXiv:2502. 250:220622.6622 2025. [19] Azka Gilani, Shah Rukh Qasim, Imran Malik, and Faisal Shafait. Table detection using deep learning. 2017 14th IAPR international conference on document analysis and recognition, volume 1, pages 771-776. IEEE, 2017.
[20] Zhangxuan Gu Changhua Meng. Ke Wang. Jun Lan, Weiqiang Wang, Ming Gu. and Liqing Zhang. XylayoutIm: Towards layout-aware multimodal networks for visually-rich document understanding. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 4583-4599 2022.
[21] Daya Guo, Dejian Yang, Haowei Zhang, Junxiao Song, Ruoyu Zhang, Runxin Xu, Qihao Zhu, Shirong Ma, Peiyi Wang, Xiao Bi, et al. Deepseek-rl: Incentivizing reasoning capability in Ilms via reinforcement learning. arXiv preprint arXiv:2501.12945 2025.
[22] Jarvis Guo, Tuney Zheng, Yuelin Bai, Bo Li, Yubo Wang, King Zhu, Yizhi Li, Graham Neubig, Wenhu Chen, and Xiang Yue. Mammoth-vl: Eliciting multimodal reasoning with instruction tuning scale. arXiv preprint arXiv:2412.0522:
2024.
[23] Vivek Gupta, Maitrey Mehta, Pegah Nokhiz, and Vivek Srikumar. Infotabs: Inference on tables as semi-structured data. arXiv preprint arXiv:200S. 06.10.617,770 2020.
[24] Vivek Gupta, Shuo Zhang, Alakananda Vempala, Yujie He, Temma Choji, and Vivek Srikumar. Right for the right reason: Evidence extraction for trustworthy tabular reasoning. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). pages 3268-3283, 2022.
[25] Kai Han, Yunhe Wang, Hanting Chen, Xinghao Chen, Jianyuan Guo, Zhenhua Liu, Yehui Tang. An Xiao, Chunjing Xu, Yixing Xu, et al. A survey on vision transformer. IEEE transactions on pattern analysis and machine intelligence, 45(1): -10-0111 2022.
[26) Jonathan Herzig, Pawel Krzysztof Nowak, Thomas Müller, Francesco Piccinno, and Julian Mar- tin Fisenschlos. Tapas: Weakly supervised table parsing via pre-training. arXiv preprint
20X::2004.022349 2020.
[27] Noah Hollmann, Samuel Müller, Lennart Purucker, Arjun Krishnakumar, Max Körfer, Shi Bin
Hoo, Robin Tibor Schirrmeister, and Frank Hutter. Accurate predictions on small data with a tabular foundation model. Nature, 637(804))311-333
2025.
[28] Jun-Peng Jiang, Si- Yi-Yang Liu, Hao-Run Cai, Qile Zhou, and Han-Jia Ye. Representation learning for tabular data: A comprehensive survey. arXiv preprint 2004 16109, 2025.
[29] Jun-Peng Jiang, Han-Jia Ye, Leye Wang, Yang Yang. Yuan Jiang, and De-Chuan Zhan. Tabular insights, visual impacts: transferring expertise from tables to images. In Proceedings of the 41st
11

---

International Conference on Machine Learning. pages 11988-22009 2024.
[30] Jun-Peng Jiang. Tao Zhou, De-Chuan Zhan, and Han-Jia Ye. Compositional condition question answering in tabular understanding. In Proceedings of the 42nd International Conference on Machine Learning, pages 27831 1-27850, 2025.
[31]
Nengzheng Jin, Joanna Siebert, Dongfang Li, and Qingeai Chen. survey on table question an- swering: recent advances. In China Conference on Knowledge Graph and Semantic Computing.
pages 174-186. Springer. 2022.
[32] Geewook Kim, Teakgyu Hong. Moonbin Yim, Jinyoung Park, Jinyeong Yim, Wonscok Hwang, Sangdoo Yun. Dongyoon Han, and Seunghyun Park. Donut: Document understanding trans-
former without ocr. arXiv preprint arXiv:21/1.1500
7(15):,, 2021.
[33] Yoonsik Kim, Moonbin Yim, and Ka Ycon Song. Tablevga-bench: A visual question answering
benchmark on multiple table domains. arXiv preprint arXiv:2404. 19205, 2024,
[34] Jumnan Li. Dongxu Li, Silvio Savarese, and Steven Hoi. Blip-2: Bootstrapping language-image pre- pre-training with frozen image encoders and large language models. In International conference on machine learning, pages 19730-1977 19742. PMLR, 2023.
[35] Minghao Li, Lei Cui, Shaohan Huang. Furu Wei, Ming Zhou, and Zhoujun Li. Tablebank: Table benchmark for image-basee based table detection and recognition. In Proceedings of the Twelfth Language Resources and Evaluation Conference, pages 1918-1925, 2020.
[36] Ming Li. Jike Zhong, Shitian Zhao, Yuxiang Lai, and Kaipeng Zhang. Think not think: A study of explicit thinking in rule-based visual reinforcement fine-tuning. arXiv preprint 1xXiv22503.16188 2025.
[37] Haotian Liu, Chunyuan Li, Qingyang Wu, and Yong Jae Lee. Visual instruction tuning. Advances
in neural information processing systems, 36, 2024.
[38] Si- Ying Liu, Hao-Run Cai, Qi-Le Zhou, and Han-Jia Ye. Talent: A tabular analytics and learning toolbox. arXiv preprint arXiv:2407.04057 2024.
[39] Si-Yang Liu and Han-Jia Ye. Tabpfn unleashed: A scalable and effective solution to tabular classification problems. In Proceedings of the 42nd International Conference on Machine Learning. pages 40043-40068, 2025.
[40] Zhenghao Liu, Haolan Wang, Xinze Li, Qiushi Xiong, Xiaocui Yang. Yu Gu, Yukun Yan, Qi Shi, Fangfang Li, Ge Yu, et al. Hippo: Enhancing the table understanding capability of large language models through hybrid-modal preference optimization. arXiv preprint urXiv:2502.17311 2025. [41] Pan Lu, Liang Qiu, Kai-Wei Chang. Ying Nian Wu, Song-Chun -huun Zhu, Tanmay Rajpurohit, Peter Clark, and Ashwin Kalyan. Dynamic prompt learning via policy gradient for semi-structured mathematical reasoning. arXiv preprint arXiv:2209. 14610, 2022.
[42] Shiyin Lu, Yang Li, Qing-Guo Chen, Zhao Xu, Weihua Luo, Kaifu Zhang, and Han-Jia Ye. Ovis: Structural embedding alignment for multimodal large language model. arXiv preprint M.Ki::2405.20797 2024.
[43] Shiyin Lu, Yang Li, Yu Xia, Yuwei Hu, Shanshan Zhao, Yanqing Ma. Zhichao Wei, Yinglun Li, Lunhao Duan, Jianshan Zhao, et al. Ovis2. 5 technical report. arXiv preprint arXiv:2508. 11737. 2025.
[44] Anand Mishra, Shashank Shekhar, Ajeet Kumar Singh, and Anirban Chakraborty. Oer-vqa: Visual question answering by reading text in images. In 2019 international conference on document analysis and recognition, pages 947-952. IEEE, 2019.
[45] Linyong Nan, Chiachun Hsieh, Ziming Mao, Xi Victoria Lin, Neha Verma, Rui Zhang, Wojeiech Kryšciniski, Hailey Schoelkopf, Riley Kong, Xiangru Tang, et al. Fetaqa: Free-form table question answering. Transactions of the Association for Computational Linguistics, 10:35 49,
2022.
[46] Ahmed Nassar, Nikolaos Livathinos, Maksym Lysak, and Peter Staar. Tableformer: Table structure understanding with transformers. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 4614-4623, 2022.
[47]
OpenAI. Gpt-4o: Hello got-4o. Technical report, OpenAI. 2024.
[48]
OpenAl. Learning to reason with 1lms. Technical report, OpenAI, 2024.
[49]
Long Ouyang, Jeffrey Wu, Xu Jiang, Diogo Almeida, Carroll Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, et al. Training language models to follow instructions with human feedback. Advances in neural information processing systems,
35:27730-27744, 2022.
[50] Shubham Singh Paliwal, Vishwanath, Rohit Rahul, Monika Sharma, and Lovekesh Vig. Tablenet: Deep learning model for end-to-end table detection and tabular data extraction from scanned document images. In 2019 international conference on document analysis and recognition (ICDAR), pages 133, 2019.
12

---

[51]
Kishore Papineni, Salim Roukos, Todd Ward, and Wei-Jing Zhu. Bleu: method for automatic evaluation of machine translation. In Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pages 31 311-318, 2002.
[52] Panupong Pasupat and Percy Liang. Compositional semantic parsing on semi-structured tables. arXiv preprint arxiv: 503500 2015.
[53] Yingzhe Peng, Gongrui Zhang, Miaosen Zhang, Zhiyuan You, Jie Liu, Qipeng Zhu, Kai Yang, Xingzhong Xu, Xin Geng, and Xu Yang. Lmm-rl: Empowering 3b Imms with strong reasoning 2025. QwenTeam. Qvq: To see the world with wisdom. Technical report, QwenTeam, Alibaba, 2024. QwenTeam. Qwq: Reflect deeply on the boundaries of the unknown. Technical report.
abilities through two-stage rule-based rl. arXiv preprint 2rXiv:2503.075..5
[54]
[55]
QwenTeam, Alibaba. 2024.
[56]
Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al. Learning transferable visual models from natural language supervision. In International conference machine learning, pages 8748-8766 PMLR, 2021.
[57] Rafael Rafailov, Archit Sharma, Eric Mitchell, Christopher D Manning, Stefano Ermon, and Chelsea Finn. Direct preference optimization: Your language model is secretly a reward model.
Advances in Neural Information Processing Systems, 36:53728-53777 2023.
[58] Mahmoud Salaheldin Kasem, Abdelrahman Abdallah, Alexander Berendeyev. Ebrahem Elkady, Mohamed Mahmoud, Mahmoud Abdalla, Mohamed Hamada, Sebastiano Vascon, Daniyar Nurseitov, and Islam Taj-Eddin. Deep learning for table detection and structure recognition: A survey. ACM Computing Surveys, 56(12):1-4) 2024.
[59]
Sebastian Schreiber, Stefan Agne, Ivo Wolf, Andreas Dengel, and Sheraz Ahmed. Deepdesrt: Deep learning for detection and structure recognition of tables in document images. In 2017 14th IAPR international conference on document analysis and recognition, volume 1, pages
1162-1167. IRFE, 2017.
[60]
John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, and Oleg Klimov. Proximal
policy optimization algorithms. arXiv preprint arXiv: (Tf.:::777.06377,, 2017.
[61]
Zhihong Shao, Peiyi Wang. Qihao Zhu, Runxin Xu, Junxiao Song, Xiao Bi, Haowei Zhang, Mingchuan Zhang, YK Li, Y Wu, et al. Deepseekmath: Pushing the limits of mathematical reasoning in open language models. arXiv preprint arXiv:2402. 030000 2024.
[62] Wenhao Shi, Zhiqiang Hu, Yi Bin, Junhua Liu, Yang Yang, See- Kiong Ng, Lidong Bing, and Roy Ka-Wei Lee. Math-llava: Bootstrapping mathematical reasoning for multimodal large language models. arXiv preprint arXiv:2406. /7294, 2024.
[63] Wenqi Shi, Ran Xu, Yuchen Zhuang, Yue Yu, Jieyu Zhang, Hang Wu, Yuanda Zhu, Joyce Ho, Carl Yang, and May Wang. Ehragent: Code empowers large language models for few-shot complex tabular reasoning on electronic health records. arXiv preprint arXiv:2401.077 128 2024. Alexey Shigarov. Table understanding: Problem overview. Wiley Interdisciplinary Reviews:
[64]
Data Mining and Knowledge Discovery, :311))e1442. 2023.
[65] Aofeng Su. Aowen Wang, Chao Ye, Chen Zhou, Ga Zhang, Gang Chen, Guangcheng Zhu, Haobo Wang. Haokai Xu, Hao Chen, et al. Tablegpt2: A large multimodal model with tabular
data integration. arXiv preprint arXiv:24/1.02055 2024.
[66] Hai-Long Sun, Zhun Sun, Houwen Peng, and Han-Jia Ye. Mitigating visual forgetting via take-along visual conditioning for multi-modal long cot reasoning. In ACL, 2025.
[67] Hai-Long Sun, Da- Zhou, Yang Li, Shiyin Lu, Chao Yi, Qing-Guo Chen, Zhao Xu, Weihua Luo, Kaifu Zhang. De-Chuan Zhan, et al. Parrot: Multilingual visual instruction tuning. In International conference on machine learning, 2025.
[68] Quan Sun, Yuxin Fang, Ledell Wu, Xinlong Wang. and Yue Cao. Eva-clip: Improved training
techniques for clip at scale. arXiv preprint arXiv:2303. 230.2339.//89 2023.
[69] Alon Talmor, Ori Yoran, Amnon Catav, Dan Lahav, Yizhong Wang, Akari Asai, Gabriel Ilharco, Hannanch Hajishirzi, and Jonathan Berant. Multimodalqa: Complex question answering over
text, tables and images. arXiv preprint arXiv:2104.0600 06039, 2021.
[70] Gemini Team, Rohan Anil, Sebastian Borgeaud, Jean- Baptiste Alayrac, Jiahui Yu, Radu Soricut, Johan Schalkwyk, Andrew M Dai, Anja Hauth, Katie Millican, et al. Gemini: a family of highly
capable multimodal models. arXiv preprint arxivv2312.... 1800, 2023.
[71] Vladimir Vapnik, Rauf Izmailov, al. Learning using privileged information: similarity control
and knowledge transfer. J. Mach. Learn. Res., 16(1):2023-0049 2015.
[72] Vladimir Vapnik and Akshay Vashist. A new learning paradigm: Learning using privileged
information. Neural networks, 22(566):544-557 2009.
[73] Jianqiang Wan, Sibo Song, Wenwen Yu, Yuliang Liu, Wenqing Cheng, Fei Huang, Xiang Bai, Cong Yao, and Zhibo Yang. Omniparser: A unified framework for text spotting key information
13

---

extraction and table recognition. In Proceedings of the IEEE/CVF Conference on Computer
Vision and Pattern Recognition, pages 15641-15653, 2024.
[74] Yibo Wang, Guangda Huzhang, Bing-Guo Chen, Zhao Xu, Weihua Luo, Kaifu Zhang, and
Lijun Zhang. SPACE: Noise contrastive estimation stabilizes self-play fine-tuning for large language models. In Advances in Neural Information Processing Systems 39, 2025.
[75] Yibo Wang, Hai-Long Sun, Guangda Huzhang, Qing-Gua Bing-Go Chen, Zhao Xu, Weihua Luo, Kaifu Zhang, and Lijun Zhang. Triplets better than pairs: Towards stable and effective self-play fine-tuning for L.L.s.. In Advances in Neural Information Processing Systems 39, 2025.
[76] Zhiruo Wang, Haoyu Dong, Ran Jia, Jia Li, Zhiyi Fu, Shi Han, and Dongmei Zhang. Tuta: Tree-based transformers for generally structured table pre-training. In Proceedings of the 27th Knowledge Discovery & Data Mining, pages 1780-1790, 2021. [77] Zilong Wang, Hao Zhang, Chun-Liang Li, Julian Martin Eisenschlos, Vincent Perot, Zifeng Wang, Lesly Miculicich, Yasuhisa Fujii, Jingbo Shang. Chen-Yu Yu Lee, et al. Chain-of-table: Evolving tables in the reasoning chain for table understanding. arXiv preprint arXiv:2401 04398, 2024.
ACM SIGKDD Conference
[78] Jason Wei, Xuezhi Wang. Dale Schuurmans, Maarten Bosma, Fei Xia, Ed Chi, Quoe V Le, Denny Zhou, et al. Chain-of -f-thuughtt prompting elicits reasoning in large language models. Advances in neural information processing systems, 35:24824 2-248724448877, 2022.
[79] An Yang. Baosong Yang. Beichen Zhang. Binyuan Hui, Bo Zheng. Bowen Yu, Chengyuan
Li, Dayiheng Liu, Fei Huang, Haoran Wei, et al. Qwen2. 5 technical report. arXiv preprint arXiv:2412. 15115, 2024.
[80] Bohao Yang, Yingji Zhang, Dong Liu, André Freitas, and Chenghua Lin. Does table source matter? benchmarking and improving multimodal scientific table understanding and reasoning. arXiv preprint Xiv: 2022551133444 13042, 2025.
[81] Yuan Yao, Tianyu Yu, Ao Zhang, Chongyi Wang, Junbo Cui, Hongji Zhu, Tianchi Cai, Haoyu Li, Weilin Zhao, Zhihui He, et al. Minicpm-v: gpt-4v level milm on your phone. arXiv preprint arXiv:2408.0 0800, 2024.
[82] Han-Jia Ye, Si-Yang Liu, Hao-Run Cai, Qi-Le Zhou, and De-Chuan Zhan. A closer look at deep learning on tabular data. arXiv preprint arXiv:2407.0098 .00956, 2024.
[83] Han-Jia Ye, Si- Yang Liu, and Wei-Lun Chao, A closer look at tabpfn v2: Strength, limitation, and extension. arXiv preprint arXiv:2502. /7361, 2025.
[84] Han-Jia Ye, Huai-Hone Hong Yin, De-Chuan Zhan, and Wei Chao. Revisiting nearest neighbor for tabular data: A deep tabular bascline two decades later. In International Conference on Learning Representations, 2025.
[85] Yunhu Ye, Binyuan Hui, Min Yang, Binhua Li, Fei Huang, and Yongbin Li. Large language models are versatile decomposers: Decomposing evidence and questions for table-based rea- soning. In Proceedings of the 46th international ACM SIGIR conference on research and development in information retrieval, pages 184, 2023.
[86] Xiang Yue, Yuansheng Ni, Kai Zhang, Tianyu Zheng, Ruoqi Liu, Ge Zhang, Samuel Stevens, Dongfu Jiang, Weiming Ren, Yuxuan Sun, al. Mmmu: A massive musti-discipline multimodal understanding and reasoning benchmark for expert agi. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 9556-9567, 2024.
[87] Xiaohua Zhai, Basil Mustafa, Alexander Kolesnikov, and Lucas Beyer. Sigmoid loss for language image pre-training. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 1 11975-11986, 2023.
[88] Renrui Zhang, Xinyu Wei, Dongzhi Jiang, Yichi Zhang, Ziyu Guo, Chengzhuo Tong, Jiaming Liu, Aojun Zhou, Bin Wei, Shanghang Zhang, et al. Mavis: Mathematical visual instruction tuning. arXiv e-prints. pages arxiv-2407, 2024.
[89] Xuanliang Zhang, Dingzirui Wang, Longxu Dou, Qingfu Zhu, and Wanxiang Che. A survey of table reasoning with large language models. Frontiers of Computer Science, 19(9):19934)
2025.
[90] Weichao Zhao, Hao Feng Qi Liu, Jingqun Tang. Binghong Wu, Lei Liao, Shu Wei, Yongjie Ye, Hao Liu, Wengang Zhou, et al. Tabpedia: Towards comprehensive visual table understanding with concept synergy. In Advances in Neural Information Processing Systems, pages 7185-7212, 2024.
[91] Yilun Zhao, Boyu Mi, Zhenting Qi, Linyong Nan, Minghao Guo, Arman Cohan, and Dragomir Radev. Openrt: an open-source framework for reasoning over tabular data. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations), pages 336-347, 2023.
[92] Mingyu Zheng, Xinwei Feng, Qingyi Si, Qiaoqiao She, Zheng Lin, Wenbin Jiang, and Weiping Wang. Multimodal table understanding. In Proceedings of the 62nd Annual Meeting of the
14

---

Association for Computational Linguistics ( Vooluee 1: Long Papers). pages 9102-9124, 2024,
[93] Deyao Zhu, Jun Chen, Xiaoqian Shen, Xiang Li, and Mohamed Elhoseiny. Minigpt-4: En- hancing asion-language understanding with advanced large language models. arXiv preprint ar3iv:2304. 10592, 2023.
[94] Fengbin Zhu, Wenqiang Lei, Youcheng Huang, Chao Wang, Shuo Zhang, Jiancheng Lv, Fuli Feng, and Tat-Seng Chua. Tat-qa: A question answering benchmark on a bybrid of tabular and textual content finance. arXiv preprint arXiv:2105. 205.:2100.07624. 2021.
[95] Fengbin Zhu, Ziyang Liu, Fuli Feng, Chao Wang. Moxin Li, and Tat Seng Chua. Tat-llm: A specialized language model for discrete reasoning over financial tabular and textual data. In Proceedings of the 5th ACM International Conference on Al in Finance, pages 310-318, 2024. [96] Wenwen Zhuang, Xin Huang, Xiantao Zhang, and Jin Zeng. Math-puma: Progressive up- ward multimodal alignment enhance mathematical reasoning. In Proceedings of the AAAI Conference on Artificial Intelligence, pages (6183-26191 2025.
15

---

A Training Details
In this section, we will introduce some training details about our experiment, including the datasets we use and the hyperparameters in our experiment.
A.1 Dataset Details
Following HIPPO [40], we use Table Question Answering (TQA) and Table Fact Verification (TFV) tasks for training and evaluation.
All data statistics are shown in Table
use five representative datasets: TABMWP EJ WTQ 52
HiTab [2]. and TAT-QA [94]. We export FeTaQA [45 due to its evaluation metric being BLEU S1 which does focus the accuracy of question answering. For Table Fact Verification (TFV) tasks,
we include TabFact [IO] and InfoTabs [23]. To further assess the robustness of our approach, we
conduct additional experiments on MMMU [6)] evaluating our model on all table-related questions within this challenging multimodal benchmark.
Table 2: Dataset Statistics.
Dataset
Train
Test
Question Answering
TABMWP
30,745
7,686
WTQ
17,689
4344
HiTab 8,941
1,586
TAT-QA 5,920
772
Fact Verification
TabFact 31,321
6,845
InfoTabs 18,383
5,400
MMMU
165
The construction of the TURBO training dataset utilized TABMWP, WTQ, TAT-QA, TabFact, and InfoTabs. The HiTab dataset is excluded because it involves multi-level tables, which present formatting challenges when converted to Markdown. From each of the chosen datasets, randomly extract 2,000 instances, resulting in a combined dataset of 10,000 training instances. Each instance consists of a structured table, question, and the corresponding answer, which then fed into structure-awe aware reasoning tracer generator to generate high-qualit reasoning traces. Then, reject sampling is used to further filter the data, resulting in 9,000 training instances in total. This dataset forms the foundation for supervised fine-tuning and subsequent reinforcement learning stages in the TURBO framework.
A.2 Hyperparameters.
As shown in Table we provide the training hyperparameters for TURBO. Throughout all stages of training, we pre-train for one epoch in 9k generated data, with a batch size of 128.
During the SFT (supervised fine-tuning) stage, adopt learning rate of 2e-6 with cosine decay schedule, a per-GPU batch size of 2, and 16 gradient accumulation steps across 4 A 1100-80G GPUs. The model is trained with bfl6 precision and gradient checkpointing enabled. The maximum text and multimodal input lengths are set to 1500 and 4096 tokens, respectively.
In the RL stage, we fine-tune using a lower learning rate of 5e-7 with a constant schedule. The setup involves 8 A100-80G GPUs with per-GPU batch size and same accumulation steps. Each input prompt generates 16 candidate completions, and we apply a CLIP range of 0.2 to stabilize reward scaling. The sequence lengths are extended 2000 (text) and 4596 (multimodal). We retain the AdamW W optimizer and warmup ratio of 0.1 across both stages, with no weight decay.
16

---

Table 3: Training hyperparameters.
Config
SFT Stage
RL Stage
Epoch
Optimizer
AdamW
Learning rate
2c-6
Sc-7 Constant
Learning rate scheduler
Cosine
Weight decay
0.0
Warmup ratio
0.1
Text max length
1500
2000
Multimodal max length
4096
4596
Generation number
CLIP range
0.2
Batch size per GPU
2
Gradient accmulation steps
16
GPU
4 A100-80G
x A100-80G
Precision
Bf16
Gradient checkpoint
True
B More Experiments
In this section, present additional experimental results further validate the effectiveness of TURBO. We also include qualitative examples to illustrate the model's behavior and capabilities across various scenarios.
B.1 Comparison with Reasoning Models
We conducted additional experiments to compare our 8B TURBO model against leading large-scale, reasoning- focused models, even those that operate on structured text inputs, which an inherently casier task. We compared TURBO with DeepSeek-RI [21] (B7IB), state-of-the-arr reasoning LLM, and QvQ (54] (72B), another powerful model designed by Qwen team known for its reasoning capabilities. We also show some text-only results for reference based on our model. The result is shown in T
Table 4: Comparison with DeepSeek and the text-only results over datasets.
Question Answering HiTab
Fact Verification TabFact
Method
Average
TABMWP
WTQ
TAT-QA
InfoTabs
DeepSeek-RI
98.31
85.03
82.86
86.79
91.61
79.46
87.34
QvQ
96.38
79.54
79.03
78.26
93.25
74.91
83.56
Baseline-textonly
94.52
62.44
75.11
66.96
82.04
72.43
75.58
TURBO-textonly
96.29
66.50
71.31
73.21
87.74
82.44
79.58
TURBO
96.75
67.80
72.15
73.21
85.81
81.89
79.60
This comparison leads to several important conclusions:
Remarkable Performance Despite Modality Disadvantage: TURBO operates on raw table images, which requires to first solve the complex perception problem of parsing the table structure and content before it can even begin reason. In contrast, models like DeepSeek-RI are given clean, structured Markdown tables-the very privileged information we use only during training. Despite this significant disadvantage in input modality, our 8B model achieves performance that is highly competitive with a 671 1B model, and even surpasses DeepSeek- RI on the InfoTabs dataset. Our model also surpasses QvQ TABMWP and Info datasets with smaller model size, which is larger and stronger reasoning model.
Exceptional Parameter Efficiency: Our TURBO framework achieves these strong results with model that is nearly times smaller than DeepSeek-RI (8B vs. 671B). This demonstrates that our proposed training methodology is highly effective at distilling complex reasoning abilities into
17

---

much more efficient model. The strength of TURBO is not in model scale, but in its novel technique for bridging the modality gap and transferring reasoning skills.
Validation of our Core Claim: This comparison strongly validates our central thesis: by leveraging privileged information during training, can equip moderately-sized MILM with reasoning capabilities on visual tables that approach, and in some cases exceed, those of massive LLMs working with clean, structured data.
Furthermore, we evaluate the generalization capability of our approach by comparing performance under text-only setting. Specifically, we run both baseline and our TURBO model using structured tables as input. Results show that TURBO, though primarily trained in a multimodal setting, achieves strong performance even when only textual input is provided. The comparable results between TURBO and its text-only variant indicate that our training strategy successfully distills and transfers structured knowledge from tables into the model. This highlights the broader applicability of our approach and its ability to enhance both visual and textual reasoning in a unified framework.
B.2 Extension of TURBO
As well-established model, Ovis2 provides clean and controlled baseline, allowing to isolate and clearly measure the specific performance gain contributed by our TURBO framework. By applying methodology to solid but not nop-performing model, we can more transparently demonstrate the effectiveness of our privileged information training and two-stage learning process itself, rather than relying on the sheer power of underlying SOTA model.
However, to directly address the question about the effectiveness our framework and to test the generalizability of our approach, we conducted pipeline to the stronger Qwen2.5- VL model. The results in Table 5 are highly encouraging and strongly validate the robustness of our framework.
experiment applying the full TurBo training
Table 5: Extension of our TURBO framework to Qwen2.5-VL,
TABMWP
WTQ
HiTab
TAT-QA
TabFact
InfoTabs
Qwen2.55 VL
92.48
65.85
67.09
70.54
83.01
77.91
-wen2.5-VL-SFT
96.02
69.77
69.73
72.59
85.92
78.17
Qwen2.5-VVL-ST--RRPP
96.92
70.35
71.64
71.55
87.27
79.93
This experiment leads to two important conclusions: 1) Applying our framework to the already stronger Qwen2.5-VI. model yields consistent and even slightly larger absolute performance gain. This demonstrates that our method of leveraging privileged information provides a fundamental enhancement tabular reasoning capabilities, regardless of the underlying base model's initial strength. 2) By combining our TURBO framework with a more powerful base model, we achieve better results. This result showeases the scalability of our approach and its potential to push the boundaries of multimodal table reasoning even further when applied to future, more powerful base models.
B.3 Inference efficiency
Inference efficiency a critical aspect of any deployable model. We have analyzed the inference speed of TURBO and can provide the following clarification.
Our two-stage training process (SFT + RL) only updates the weights of the model; it does not alter the model's architecture or increase its parameter count. Therefore, the fundamental per-token generation speed of our TURBO model identical to that the original Ovis2 baseline. The model itself is not inherently slower.
The observable increase in total inference time for TURBO comes from a deliberate design choice: the model generates longer output sequence. Instead of producing only final answer, TURBO generates full reasoning trace. This naturally takes more time because more tokens need to be generated.
18

---

To provide concrete data, we measured the average inference time and output length on the WTQ dataset in Table o
Table 6: Inference time in WTQ dataset.
Model
Avg. Inference Time per Sample (A100-80G)
Ovis2 (Baseline)
0.8 seconds
TURBO
2.7 seconds
QvQ-72B
108 seconds
This data highlights a clear trade-off:: TURBO'S inference time is longer than its own baseline's because it's performing and articulating complex reasoning process. This the cost of achieving significantly higher accuracy and providing a traceable output for error analysis.
Competitive Speed Compared to Other SOTA Models: Crucially, when compare TURBO's inference speed to that of other powerful, state-of-the-an multimodal models like QvQ. we find that inference time not outlier and remains highly competitive. Despite generating detailed reasoning trace, TURBO'S 's speed is well within the acceptable limits for models this performance class. This demonstrates that the efficiency cost for our significant accuracy gains is reasonable and practical.
In summary, the two-stage training does not slow down the model's intrinsic speed. The longer inference time a direct and predictable consequence generating more detailed, reasoned outputs. This represents a favorable trade-off for achieving state-of-the-at accuracy, and the resulting speed is comparable to other leading models.
B.4
More Cases
We present more detailed cases in Figure 0 These cases illustrate the diverse reasoning types and challenges, including multi-step arithmetic, compositional conditions, and fine- aee-grained table comprehension. These cases demonstrate that our TURBO can do well in both visual grounding, mathematics, and logical reasoning.
C Limitations and Future Works
Failure cases. Based on thorough review of our model's 's outputs, we have identified two primary categories of failure cases.
The first step in visual table reasoning is accurately extracting the textual content. Like most MILMs, TURBO is susceptible occasional OCR errors, especially with dense text, small fonts, or image artifacts. These are often subtle but critical. For example, we observed cases where number like "30001" was misidentified as "3001" or a keyword was misread. Such low-level perception errors inevitably lead to incorrect results in subsequent reasoning steps, as the model is operating on faulty input data.
OCR Errors in Content Extraction:
Errors in Complex Numerical or Logical Reasoning: Even when all table data is extracted perfectly, TURBO sometimes fail at the final reasoning stage. This is known challenge for large language models in general. We observed that these errors are most common in tasks requiring multi-step arithmetic calculations formulate a plan to sum several cells and then subtract a value, but make a mistake during the final calculation. This indicates that while our method significantly enhances reasoning capabilities, does not completely climinate the inherent fallibility of LLMs in complex computation.
complex logical deductions. For instance, the model might correctly
Limitations. While this work focuses on scenarios where structured tables are unavailable at test Time-requiring inference purely from table images- there exist many practical situations where both the image and its underlying structured representation can be accessed. Exploring how to effectively incorporate structured tables as additional inputs during inference presents a promising direction for future research.
Another limitation lies in the type of tables considered. Most of our experiments involve relatively clean and regular tables, where each row corresponds to a distinct entity and each column to specific
19

---

Question: cundy dispenser put various numbers of orange candies into bags. How many bags had least 22 orange candies but fewer than orange cundies?
let's see. The problem usking how many bags had at least 22 orunge candies but fewer than 86. The data is given stem-and-led plot. First, need understand leaf is the ones place. So, example, stem of with leaves 8, would
Turbo:<think-
Orange candies per bag
Samm
Luw
stem-and-leuf plots work. stem represents the tens place, 18, Looking the table, ... the stems that fall within this range are 2. 4, and Stem 89, which is above Now, let's count the leaves for each of these stems:'n'n- Stem Leaves are 1, 9. That's 21, 26, 29, but 21 should be excluded. So bags
68.
3
9
that's excluded.
12
Adding these up: 2 86 candies is 11, 1/tt/think>
+ 2 11. So the total number of bags with at least 22 but fewer tham
<answer- </nswer>>>
Question: The results of compaction
Saapl/ No.
Malovar CoAsee
Ball Ducaty 3ples
sumples of soil that be used for embankment a highway project are listed below. Determine the optimum moisture content.
48
2.064
7.5
833
2,543
A: 10%.
B: 8%.
C: 9%
Turbo: <think-Okay, let's The question asking for optimum moisture results provided in the table. The table lists five samples with their respective moisture content and bulk density. First, need to understand what l'optimum moisture content:" means. In soil compaction, the optimum moisture content is the percentage at which the soil can be compacted to achieve the maximum dry density. Looking at the table, the samples are numbered to S. The moisture content increases from 4.8% to 9.7%. The bulk density values ulso increase, starting from 2.164 g/ee and going up to 2.351 g/ee, ...
based the compaction
The key here to find the sumple where the bulk density is highest, us that would correspond to the optimum moisture content. Let's check the bulk density values:'n'a- Sample I: S....pl' has the highest bulk density at 2.351 g/oe. which is the maximum value in the table. Therefore, optimum moisture content 7,8%, which closest option B: stiinii <answer-B</answeer
Figure 6: More cases of Turbo.
attribute. However, real-world tables are often more diverse and complex. For example, datasets like HiTab and MMMU include tables with merged cells, nested headers, and irregular layouts that challenge current MLLMs. more systematic analysis of how different table structures affect multimodal reasoning performance would be valuable extension of this work.
Future work. Lastly, while our proposed TURBO framework significantly improves the reasoning abilities of open-source MILMs, substantial performance gap remains when compared with state- of-the-art proprietary L.LMs such as DeepSeek. We hope this paper inspires the community to further explore real-world multimodal reasoning scenarios and to continue narrowing the gap between open- and closed-source models, contributing to more robust and accessible MLLMs.
20

---

NeurIPS Paper Checklist
1. Claims
Question: Do the main claims made in the abstract and introduction accurately reflect the paper's contributions and scope?
Answer: [Yes]
Justification:
The abstract and introduction include the claims made in the paper.
Guidelines:
The answer NA means that the abstract and introduction do not include the claims made in the paper.
The abstract and/or introduction should clearly state the claims made, including the contributions made in the paper and important assumptions and limitations. A No or
NA answer to this question will not perceived well by the reviewers.
The claims made should match theoretical and experimental results, and reflect how much the results
be expected to generalize other settings.
is fine to include aspirational goals as motivation as long is clear that these goals are not attained by the paper.
2. Limitations
Question: Does the paper discuss the limitations of the work performed by the authors?
Answer: [Yes]
Justification:
The limitations are discussed in Section
Guidelines:
The answer NA means that the paper has no limitation while the answer No means that the paper has limitations, but those not discussed in the paper.
The authors are encouraged to create separate "Limitations" section in their paper.
The paper should point out any strong assumptions and how robust the results are to violations of these assumptions (e.g., independence assumptions, noiseless settings, model well-specification. asymptotic approximations only holding locally). The authors should reflect how these assumptions might be violated in practice and what the implications would be.
The authors should reflect on the scope of the claims made, e.g., if the approach was only tested on few datasets or with few runs. In general, empirical results often depend on implicit assumptions, which should be articulated.
The authors should reflect the factors that influence the performance of the approach. For example, facial recognition algorithm may perform poorly when image resolution low images are taken in low lighting. Or speech-to-text system might be used reliably to provide closed captions for online lectures because it fails to handle technical jargon.
The authors should discuss the computational efficiency of the proposed algorithms and how they scale with dataset size.
If applicable, the authors should discuss possible limitations of their approach address problems of privacy and faimess.
While the authors might fear that complete honesty about limitations might be used by reviewers as grounds for rejection, a worse outcome might be that reviewers discover limitations that aren't 't acknowledged in the paper. The authors should use their best judgment and recognize that individual actions in favor of transparency play an impor- tant role in developing norms that preserve the integrity of the community. Reviewers will be specifically instructed to not penalize honesty concerning limitations.
3. Theory assumptions and proofs
Question: For each theoretical result, does the paper provide the full set of assumptions and complete (and correct) proof?
Answer: [NA]
21

---

Justification:
There are no theoretical results in this paper.
Guidelines:
The answer NA means that the paper does not include theoretical results.
All the theorems, formulas, and proofs in the paper should be numbered and cross- referenced.
All assumptions should be clearly stated or referenced the statement of any theorems.
The proofs can either appear the main paper or the supplemental material, but they appear in the supplemental material, the authors are encouraged to provide short proof sketch to provide intuition.
Inversely, any informal proof provided in the core of the paper should be complemented by formal proofs provided in appendix supplemental material.
Theorems and Lemmas that the proof relies upon should be properly referenced.
4. Experimental result reproducibility
Does the paper fully disclose all the information needed to reproduce the main ex- perimental results of the paper to the extent that it affects the main claims and/or conclusions of the paper (regardless of whether the code and data are provided or not)?
Question:
Answer: [Yes]
Justification: All information needed to reproduce the main experimental results of the paper is available and easy follow.
Guidelines:
The answer NA means that the paper does not include experiments.
If the paper includes experiments, No answer to this question will not be perceived well by the reviewers: Making the paper reproducible is important, regardless of whether the code and data are provided or not.
If the contribution is dataset and/or model, the authors should describe the steps taken to make their results reproducible or verifiable.
Depending on the contribution, reproducibility can be accomplished in various ways. For example, if the contribution is novel architecture, describing the architecture fully might suffice, or if the contribution is a specific model and empirical evaluation, it may be necessary to either make possible for others to replicate the model with the same dataset, or provide access to the model. In general. releasing code and data is often one good way accomplish this, but reproducibility can also be provided via detailed instructions for how to replicate the results, access to a hosted model (e.g.. in the case of large language model), releasing of a model checkpoint, or other means that are appropriate to the research performed.
While NeurIPS does not require releasing code, the conference does require all submis- sions to provide some reasonable avenue for reproducibility, which may depend on the nature of the contribution. For example
(a) If the contribution is primarily a new algorithm, the paper should make it clear how reproduce that algorithm. (b) If the contribution is primarily
model architecture, the paper should describe the architecture clearly and fully.
(c) If the contribution is a new model (e.g., a large language model), then there should either be way to access this model for reproducing the results or way to reproduce the model (e.g., with an open-source dataset or instructions for how to construct the dataset).
(d) We recognize that reproducibility may be tricky in some cases, in which case authors are welcome describe the particular way they provide for reproducibility, In the case of closed-source models, it may be that access to the model is limited in some way (e.g., registered users), but it should be possible for other researchers to have some path to reproducing or verifying the results.
5. Open access to data and code
Question: Does the paper provide open access to the data and code, with sufficient instruc- tions to faithfully reproduce the main experimental results, as described in supplemental material?
22

---

Answer:
[Yes]
The data is available. The code is easy to follow according to existing repositories.
Justification:
Guidelines:
The answer NA means that paper does not include experiments requiring code.
Please see the NeurIPS code and data submission guidelines (https://nips.cm ec/
for more details.
While encourage the release of code and data, understand that this might not be possible, so "No" is an acceptable answer. Papers cannot be rejected simply for not including code, unless this is central to the contribution (e.g., for new open-source benchmark).
The instructions should contain the exact command and environment needed to run to reproduce the results. See the NeurlPS code and data submission guidelines (https for more details.
//nips. //nips.cc/pil wwcppbblc/guuiicuccccccccoe.oopp /etgguddsse/CeSbbbiiiinn
The authors should provide instructions data access and preparation, including how to access the raw data, preprocessed data, intermediate data, and generated data, etc.
The authors should provide scripts to reproduce all experimental results for the new proposed method and baselines. only subset of experiments are reproducible, they should state which ones are omitted from the script and why.
At submission time, preserve anonymity, the authors should release anonymized versions (if applicable).
Providing as much information as possible in supplemental material (appended to the paper) recommended, but including URLs to data and code permitted.
6. Experimental setting/details
Question: Does the paper specify all the training and test details (e.g., data splits, hyper- parameters, how they were chosen, type of optimizer, etc.) necessary to understand the results?
Answer: [Yes]
Justification: All training details are in the Appendix A
Guidelines:
The answer NA means that the paper does not include experiments.
The experimental setting should be presented in the core of the paper level of detail that is necessary to appreciate the results and make sense of them.
The full details can be provided either with the code, in appendix, as supplemental material.
7. Experiment statistical significance
Does the paper report error bars suitably and correctly defined or other appropriate information about the statistical significance of the experiments?
Question:
Answer:
[NA]
Justification:
The evaluation of LLM and MLLMs does not report error bars.
Guidelines:
The answer NA means that the paper does not include experiments.
The authors should answer "Yes" if the results are accompanied by error bars, confi- dence intervals, statistical significance tests, least for the experiments that support the main claims of the paper.
The factors of variability that the enror bars are capturing should be clearly stated (for example, train/test split, initialization, random drawing of some parameter, or overall with given experimental conditions).
The method for calculating the error bars should be explained (closed form formula, call to a library function, bootstrap, etc.)
The assumptions made should be given (e.g., Normally distributed errors).
23

---

It should be clear whether the error bar is the standard deviation or the standard error of the mean.
is OK to report 1-sigma error bars, but one should state it. The authors should preferably report a 2-sigma error bar than state that they have 96% CI, if the hypothesis of Normality of errors is not verified.
For asymmetric distributions, the authors should be careful not to show in tables figures symmetric error bars that would yield results that are out range (e.g. negative error rates).
error bars are reported in tables or plots, The authors should explain the text how they were calculated and reference the corresponding figures tables in the text.
8. Experiments compute resources
For each experiment, does the paper provide sufficient information on the com- puter resources (type of compute workers, memory, time of execution) needed to reproduce the experiments?
Question:
Answer: [Yes]
Justification:
The information is provided in Section 5
Guidelines:
The answer NA means that the paper does not include experiments.
The paper should indicate the type of compute workers CPU or GPU, internal cluster, cloud provider, including relevant memory and storage.
The paper should provide the amount compute required for each of the individual experimental rums well estimate the total compute.
The paper should disclose whether the full research project required more compute than the experiments reported in the paper (e.g., preliminary or failed experiments that didn't make into the paper).
9. Code of ethics
Question: Does the research conducted in the paper conform, in every respect, with the NeurlPS Code of Ethics htappp:////urips.../// httpripp.../pbllleehhccccceeeee.
Answer: [Yes]
Justification: Yes.
Guidelines:
The answer NA means that the authors have reviewed the NeurIPS Code of Ethics.
If the authors answer No, they should explain the special circumstances that require deviation from the Code of Ethics.
The authors should make sure to preserve anonymity (e.g., there a special consid- eration due to laws regulations in their jurisdiction).
10. Broader impacts
Answer: [NA]
This paper presents work whose goal is to advance the field Machine Learning, especially MILMss and tabular reasoning. There many potential societal consequences in our work, none of which we feel must be specifically highlighted here.
Justification:
Guidelines:
The answer NA means that there is no societal impact of the work performed.
If the authors answer NA or No. they should explain why their work has no socictal impact why the paper does not address societal impact.
Examples of negative societal impacts include potential malicious or unintended uses (e.g., disinformation, generating fake profiles, surveillance), fairness considerations (e.g., deployment of technologies that could make decisions that unfairly impact specific groups), privacy considerations, and security considerations.

---

The conference expects that many papers will be foundational research and not tied to particular applications, let alone deployments. However, if there is direct path to any negative applications, the authors should point it out. For example, it is legitimate to point out that an improvement in the quality of generative models could be used to generate deepfakes for disinformation. On the other hand, it is not needed to point out that a generic algorithm for optimizing neural networks could enable people to train models that generate Deepfakes faster.
The authors should consider possible harms that could arise when the technology is being used as intended and functioning correctly, harms that could arise when the technology being used as intended but gives incorrect results, and harms following from (intentional or unintentional) misuse of the technology.
If there negative societal impacts, the authors could also discuss possible mitigation strategies (e.g., gated release of models, providing defenses in addition to attacks, mechanisms for monitoring misuse, mechanisms monitor how system learns from feedback over time, improving the efficiency and accessibility of MI..
11. Safeguards
Does the paper describe safeguards that have been put in place for responsible release of data models that have high risk for misuse (e.g., pretrained language models, image generators, or scraped datasets)?
Question:
Answer: [No]
Justification:
The models and datasets here are all public and frequently used.
Guidelines:
The answer NA means that the paper poses such risks.
Released models that have high risk for misuse or dual-use should be released with necessary safeguards to allow for controlled use of the model, for example by requiring that users adhere usage guidelines or restrictions to access the model or implementing safety filters.
Datasets that have been scraped from the Internet could pose safety risks. The authors should describe how they avoided releasing unsafe images.
We recognize that providing effective safeguards is challenging, and many papers do not require this, but we encourage authors to take this into account and make best faith effort.
12. Lieenses for existing assets
Question: Are the creators or original owners of assets (e.g., code, data, models), used in the paper. properly credited and are the license and terms of use explicitly mentioned and properly respected?
Answer: [Yes]
Justification: We cite all the original papers that produced the code package and datasets.
Guidelines:
The answer NA means that the paper does not existing assets.
The authors should cite the original paper that produced the code package or dataset.
The authors should state which version of the asset is used and, if possible, include URL.
The name of the license (c.g., CC-BY 4.0) should be included for each asset.
For scraped data from a particular source (c.g., website), the copyright and terms of service of that source should be provided.
If assets are released, the license, copyright information, and terms of use in the package should be provided. For popular datasets, paperswi thcode. com com/datasets has curated licenses for some datasets. Their licensing guide can help determine the license of dataset.
For existing datasets that are re-packaged, both the original license and the license of the derived asset (if it has changed) should be provided.
25

---

If this information is not available online, the authors are encouraged to reach out to the asset's creators.
13. New assets
Question: Are new assets introduced in the paper well documented and is the documentation provided alongside the assets?
Answer: [NA]
Justification: The paper does not release new assets.
Guidelines:
The answer NA means that the paper does not release new assets.
Researchers should communicate the details of the dataset/code/mmoel as part of their submissions via structured templates. This includes details about training, license, limitations, etc.
The paper should discuss whether and how consent obtained from people whose asset is used.
At submission time, remember to anonymize your assets (if applicable). You can either create an anonymized URL or include an anonymized zip file.
14. Crowdsoureing and research with human subjects
Question: For crowdsourcing experiments and research with human subjects, does the paper include the full text of instructions given to participants and screenshots, if applicable, as well details about compensation (if any)?
Answer: [NA]
Justification: The paper does not involve crowdsourcing nor research with human subjects.
Guidelines:
The answer NA means that the paper does not involve crowdsourcing nor research with human subjects.
Including this information in the supplemental material is fine, but if the main contribu- tion of the paper involves human subjects, then as much detail as possible should be included the main paper.
According the NeurIPS Code of Ethics, workers involved in data collection, curation, or other labor should be paid at least the minimum wage in the country of the data collector.
15. Institutional review board (IRB) approvals or equivalent for research with human subjects
Question:
Does the paper describe potential risks incurred by study participants, whether such risks were disclosed to the subjects, and whether Institutional Review Board (IRB) approvals (or an equivalent approval/review based on the requirements of your country or institution) were obtained?
Answer: [NA]
Justification:
The paper does not involve potential risks incurred by study participants.
Guidelines:
The answer NA means that the paper does not involve crowdsourcing nor research with human subjects.
Depending the country in which research is conducted, IRB approval (or equivalent) may be required for any human subjects research. If you obtained IRB approval, you should clearly state this in the paper.
We recognize that the procedures for this may vary significantly between institutions and locations, and expect authors to adhere to the NeurlPS Code of Ethics and the guidelines for their institution.
For initial submissions, do not include any information that would break anonymity (if applicable), such the institution conducting the review.
16. Declaration of LLM usage
26

---

Question: Does the paper describe the usage of LLMs if it is important, original, or non-standard component of the core methods this research? Note that if the LLM used only for writing, editing, or formatting purposes and does not impact the core methodology, scientific rigorousness, or originality the research, declaration is not required.
Answer: [Yes]
Justification: This paper provide method for multimodal large language models in tabular reasoning. Details can be found Section and Section
Guidelines:
The answer NA
that the method development in this research does involve LLMs as any important, original, or non-standard components.
Please refer to our LLM policy http://///earppppcc//oofeeeeeeee for what should or should be described.
27


Reviews:
[{"review_id": "uRtlUbBDxm", "reviewer": "Reviewer_sLTv", "summary": "This paper proposes a multimodal tabular reasoning framework called TURBO: it utilizes structured table content as input to DeepSeek to generate reasoning paths corresponding to the given questions. After further improving the data quality, SFT and GRPO post-training techniques are employed to train multimodal visual understanding models, enhancing their reasoning capabilities. Based on Ovis2, results show that TURBO achieves significant improvements across multiple table understanding tasks, include TABMWP, WTQ, HiTab, TAT-QA, TabFact, InfoTabs, and MMMU.", "questions": "Please refer to the Strengths and Weaknesses.", "limitations": "Please refer to the Strengths and Weaknesses.", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 2, "strengths_and_weaknesses": "Strengths:\n\n1. The narrative of the article is clear. Overall, the proposed method corresponds well to the challenges highlighted in the paper. For example, it first introduces the limitations of multimodal table understanding, such as image information and content representation, and then proposes a two-stage training scheme—sending the table parsing results to DeepSeek to obtain QA pairs with reasoning paths. Subsequently, the visual modality is incorporated, and incremental training is conducted on VLM.\n\n2. The experimental results in the \"Main Results\" section are relatively clear. TURBO demonstrates better metrics across multiple dimensions, not only in table understanding scenarios but also in validating general capabilities.\n\n\nWeaknesses:\n\n1. The originality is relatively weak. As mentioned in part 4, TURBO mainly proposes a complete data production and training pipeline for table understanding scenarios. For instance, reasoning data is generated using reasoning-capable LLMs, and post-training of VLM employs common methods like SFT and GRPO. These all seem to be relatively conventional methods.\n\n2. In the main results (Table 1), most of the comparison methods for TURBO are non-reasoning models. From the perspective of the main experimental table, this comparison seems somewhat unfair. It would be better to include conclusions involving reasoning models.\n\n3. Is the main innovation of this paper the two-stage framework? If so, the \"Further Studies\" section (5.3) seems to lack sufficient elaboration to highlight this aspect.\n\n4. Is there a plan to contribute to community development? For example, will the related training data be further open-sourced?", "was_revised": true, "final_justification": "As my concern is solved, I would like to raise my score from 3 to 4.", "rebuttal": "We appreciate your recognition of our writing, experimental results, and your valuable comments. We will answer the questions below, and we hope this clears up your concerns.\n\n**Q1:** The originality is relatively weak. Is the main innovation of this paper the two-stage framework?\n\n**A1:**  We agree that the individual components we employ, such as SFT and GRPO, are established and powerful techniques. However, we respectfully argue that the primary novelty of TURBO lies not in the invention of these base components, but in **the novel problem formulation and the synergistic framework we designed to address a challenging and practical task**: enhancing multimodal reasoning on table *images* by leveraging structured data as *privileged information* during training.\n\nOur key contributions to novelty are:\n\n1. **A Novel Application of Learning with Privileged Information:** While the concept of privileged information exists, its application to bridge the significant modality gap between structured text (as the privileged information) and visual table images (as the inference-time input) for complex, multi-step reasoning is a novel contribution of our work. Prior works often require the structured data at inference time (e.g., HIPPO) or do not explicitly frame the problem this way. Our work directly tackles the real-world scenario where only table images are available for deployment, which is a practical and underexplored problem setting.\n2. **The Modality-Bridging Data Generation Pipeline:** Our data generation process is more than a conventional use of LLMs. We introduce a **\"structure-aware reasoning trace generator\"** (line 12) specifically designed to create high-quality, modality-invariant reasoning paths. The key insight is that while the input modalities (structured table vs. table image) differ, the underlying logical steps to answer a question should be the same. By generating these explicit reasoning traces from the privileged information and using them to supervise the MLLM, we effectively create a \"bridge\" to transfer complex reasoning skills across the modality gap. The careful application of reject sampling (lines 208-212) further ensures the quality of this bridge, a crucial step for the success of the framework.\n3. **A Synergistic and Effective Training Framework:** The originality also stems from the way our two-stage training process is tailored to this specific task.\n   - **Supervised Fine-Tuning (SFT)** on the bridged data is the foundational step that enables the MLLM to \"internalize a 'think-then-answer' paradigm\" (line 226) and learn the semantics of tabular reasoning from visual input.\n   - **Reinforcement Learning (GRPO)** then goes a step further, refining this nascent capability by allowing the model to explore a broader space of reasoning paths and reinforcing the most accurate and robust ones. As shown in our ablation study (Figure 4), both stages are complementary and crucial, leading to a significant average performance gain over the baseline.\n\nThe effectiveness of this novel combination is demonstrated by our state-of-the-art results. TURBO achieves a **+7.2% average improvement over the previous SOTA with only 9k training examples**. This substantial gain would not be possible if our work were merely a conventional application of existing methods; rather, it is direct evidence of the effectiveness of our proposed framework.\n\nWe hope this clarifies the originality and contribution of our work. We will ensure these points are further emphasized in the final version of the paper.\n\n**Q2:** It would be better to include conclusions involving reasoning models.\n\nWe conducted **additional experiments** to compare our 8B TURBO model against leading large-scale, reasoning-focused models, even those that operate on structured text inputs, which is an inherently easier task. We compared TURBO with **DeepSeek-R1 (671B)**, a state-of-the-art reasoning LLM, and **QvQ (72B)**, another powerful model designed by Qwen team known for its reasoning capabilities. The results are summarized below:\n\n| | TABMWP| WTQ| HiTab| TAT-QA | TabFact | InfoTabs  |\n|-|-|-|-|-|-|-|\n| QvQ-72B | 96.38  | 79.54  | 79.03  | 78.26  | 93.25   | 74.91|\n| DeepSeek-R1-671B | 98.31  | 85.03  | 82.86  | 86.79  | 91.61   | 79.46|\n| Turbo-8B | 96.75  | 67.80  | 72.15  | 73.21  | 85.81   | 81.89 |\n\nThis comparison leads to several important conclusions:\n\n1. **Remarkable Performance Despite Modality Disadvantage:** TURBO operates on raw table images, which requires it to first solve the complex perception problem of parsing the table structure and content before it can even begin to reason. In contrast, models like DeepSeek-R1 are given clean, structured Markdown tables—the very privileged information we use only during training. Despite this significant disadvantage in input modality, our 8B model achieves performance that is highly competitive with a 671B model, and even **surpasses DeepSeek-R1 on the InfoTabs dataset**. Our model also surpasses QvQ on TABMWP and InfoTabs datasets with smaller model size, which is a larger and stronger reasoning model.\n2. **Exceptional Parameter Efficiency:** Our TURBO framework achieves these strong results with a model that is nearly **85 times smaller** than DeepSeek-R1 (8B vs. 671B). This demonstrates that our proposed training methodology is highly effective at distilling complex reasoning abilities into a much more efficient model. The strength of TURBO is not in model scale, but in its novel technique for bridging the modality gap and transferring reasoning skills.\n3. **Validation of our Core Claim:** This comparison strongly validates our central thesis: by leveraging privileged information during training, we can equip a moderately-sized MLLM with reasoning capabilities on visual tables that approach, and in some cases exceed, those of massive LLMs working with clean, structured data.\n\n\n**Q3:** Is there a plan to contribute to community development? For example, will the related training data be further open-sourced?\n\n**A3:** We promise we will release the related training data and model checkpoint for community development."}, {"review_id": "vZle162EG2", "reviewer": "Reviewer_UWxA", "summary": "This paper aims to improve the performance of Tabular reasoning of table image with the aid of structured information. Using DeepSeek-R1, they construct structure-aware reasoning trace to get high-quality modality-bridged data. Using these data, they repeatedly generate and select advantageous reasoning paths. Experiments on several benchmarks show the effectiveness of the proposed method.", "questions": "See \"Weaknesses\".", "limitations": "Yes.", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths:\n1) The problem of tabular reasoning on table image is important since in real application the structured information is frequently unavailable, and using structured information as bridge is reasonable.\n2) The high-quality data obtained using DeepSeek-R1 is valuable, and the GRPO RL method is proposed to train the MLLMs.\n3) Experimental results on QA, Fact verification, and MMMU show the effectiveness of the proposed method.\n\nWeaknesses:\n1) The baseline is Ovis, how about the results on other baseline such as Qwen2.5VL? From Tab.1, Qwen2.5VL is superior to Ovis, does it+Turbo is superior to Ovis+Turbo?\n2) Lack of limitation discussion and failure case study. In what scenario the proposed method performs worse?", "was_revised": true, "final_justification": "My concerns about baseline generalization and method limitation are supplemented, and I maintain my previous positive rating.", "rebuttal": "We appreciate your recognition of our reasonable setting, valuable high-quality data, and effective results. We will answer the questions below, and we hope this clears up your concerns.\n\n**Q1:** The baseline is Ovis, how about the results on other baseline such as Qwen2.5VL? From Tab.1, Qwen2.5VL is superior to Ovis, does it+Turbo is superior to Ovis+Turbo?\n\n**A1:** That is an excellent and insightful question. We thank the reviewer for raising this important point about the generalizability of our framework to different base models.\n\nOur initial choice of Ovis2 as the base model was deliberate, and we would like to clarify our rationale. As a well-established model, Ovis2 provides a clean and controlled baseline, allowing us to **isolate and clearly measure the specific performance gain contributed by our TURBO framework**. By applying our methodology to a solid but not top-performing model, we can more transparently demonstrate the effectiveness of our privileged information training and two-stage learning process itself, rather than relying on the sheer power of an underlying SOTA model.\n\nHowever, to directly address the question and to test the generalizability of our approach, we have, as suggested, **conducted a new experiment applying the full TURBO training pipeline to the stronger Qwen2.5-VL model**. The results are highly encouraging and strongly validate the robustness of our framework.\n\n|                     | TABMWP | WTQ    | HiTab  | TAT-QA | TabFact | InfoTabs  |\n|---------------------|--------|--------|--------|--------|---------|-----------|\n| Qwen2.5-VL          | 92.48  | 65.85  | 67.09  | 70.54  | 83.01   | 77.91     |\n| Qwen2.5-VL-SFT      | 96.02  | 69.77  | 69.73  | 72.59  | 85.92   | 78.17     |\n| Qwen2.5-VL-SFT-GRPO | 96.92  | 70.35  | 71.64  | 71.55  | 87.27   | 79.93     |\n\nThis experiment leads to two important conclusions:\n\n1. Applying our framework to the already stronger Qwen2.5-VL model yields a **consistent and even slightly larger absolute performance gain**. This demonstrates that our method of leveraging privileged information provides a fundamental enhancement to tabular reasoning capabilities, regardless of the underlying base model's initial strength.\n\n2. By combining our TURBO framework with a more powerful base model, we achieve better results. This result showcases the scalability of our approach and its potential to push the boundaries of multimodal table reasoning even further when applied to future, more powerful base models.\n\n\n**Q2**: Lack of limitation discussion and failure case study. In what scenario the proposed method performs worse?\n\n**A2:**:  We sincerely thank the reviewer for this important and constructive feedback. A discussion of limitations and failure cases is indeed essential for a complete and balanced paper, and we appreciate the opportunity to share our analysis. \n\nBased on a thorough review of our model's outputs, we have identified three primary categories of failure cases, which we will add as a dedicated \"Limitations and Failure Case Analysis\" section in the final version of the paper.\n\n1. **Challenges with Highly Irregular or Ambiguous Table Structures:**\n   While TURBO performs well on tables with relatively clear grid-like structures, its performance degrades on tables that are structurally complex or ambiguous. This limitation is particularly relevant for datasets like HiTab and some examples in MMMU, which feature such complex layouts.\n2. **OCR Errors in Content Extraction:**\n   The first step in visual table reasoning is accurately extracting the textual content. Like most MLLMs, TURBO is susceptible to occasional OCR errors, especially with dense text, small fonts, or image artifacts. These are often subtle but critical. For example, we observed cases where a number like **\"30001\" was misidentified as \"3001\"**, or a keyword was misread. Such low-level perception errors inevitably lead to incorrect results in subsequent reasoning steps, as the model is operating on faulty input data.\n3. **Errors in Complex Numerical or Logical Reasoning:**\n   Even when all table data is extracted perfectly, TURBO can sometimes fail at the final reasoning stage. This is a known challenge for large language models in general. We observed that these errors are most common in tasks requiring **multi-step arithmetic calculations** or complex logical deductions. For instance, the model might correctly formulate a plan to sum several cells and then subtract a value, but make a mistake during the final calculation. This indicates that while our method significantly enhances reasoning capabilities, it does not completely eliminate the inherent fallibility of LLMs in complex computation.\n\n We are grateful for the reviewer's feedback, as it prompts us to make our paper more comprehensive. We will add a new section in the appendix detailing these limitations, complete with illustrative failure case examples."}, {"review_id": "MNto2itt2u", "reviewer": "Reviewer_YAdv", "summary": "The paper introduces TURBO, an approach to enhance the capabilities of multimodal large language models for tabular reasoning on table images. During training, the proposed framework leverages the tables in two modality forms: images and markdown with the goal to improve the model's capabilities to work solely on images during inference. The authors create structure-aware reasoning traces using DeepSeek-R1 and use these traces to conduct supervised fine-tuning of the multimodal language model. Afterwards, they employ reinforcement learning techniques to further enhance the tabular reasoning capabilities of the model.\nThe paper evaluates the proposed approach against several state of the art models.", "questions": "Q1) The paper and appendix describe the training set of 9k datapoints (a mix from several different dataset), but no details about the test set are mentioned. Based on which data are the experiment results (Table 1, Figure 4) reported?\n\nQ2) What also did not get clear to me: In your experiments, is each model (also the ones compared to) fine-tuned on the 9k datapoints from the training set? Please provide details on the exact setup.\n\nQ3) In Table 4 (appendix): What does the \"Baseline-textonly\" refer to?\n\nIn order to assess whether the experiments are solid, answers to the first two questions are fundamental.  Based on these answers, I am willing to raise my overall score.", "limitations": "yes", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Overall, the paper is mostly well-written and easy to follow, but the motivation and the experiments need to be explained in more detail. \n\n1) I would recommend to present the main motivation in the abstract and introduction differently. Specifically, the claim that \"_tables typically appear as images_\" (lines 4–5) is exaggerated and does not reflect the majority of real-world use cases. The cited papers (lines 26–27) do not support such a broad generalization; rather, they provide more convincing examples of scenarios in which tables are only available as images. Lines 92-93 (\"Since structured tables are rarely available in real-world settings\") are equally overstated. I think the work is not less valuable if it clearly states the (more narrower) use cases that it operates in.\n\n2) Additionally, the introduction lacks a solid justification for combining images and structured tables, which is the main contribution of the approach. I would recommend to base the introduction more on the motivation stated in Lines 128-129 in Section 3, which contain a convincing motivation.\n\n3) The terms \"bridged information\" and \"privileged structured information\" should be explained in the introduction.\n\n4) The work of HIPPO is very close to the proposed approach as it also combines table images with text based table representations. The similarities and differences between TURBO and HIPPO should be discussed in more detail in the related work section.\n\n5) The setup for the experiment in Figure 1 is not easy to understand, did I get it right, that the models are used in a zero-shot setting without being trained, once with table image + table markdown as an input, and once only receiving the table image? Please update the description in Section 3.2 and the caption of the Figure to make the setup more clear.\n\n6) Figure 2 includes a stem-leaf plot, which in my understanding is a plot and not a table and therefore not a good example to show the challenges of tabular reasoning.\n\n7) The statement in line 166 suggests that step-by-step thinking inherently leads to interpretability (\"This lack of step-by-step thinking not only undermines interpretability...\"). However, this is misleading: while such traces may give the _appearance_ of interpretability, they do not reflect the model’s actual internal processes. I would recommend just to leave this part of the phrase out of the paper as interpretability is not central to the proposed approach.\n\n8) The main experiment in Section 5.2 (Table 1) also needs to be explained in more detail. Please answer my questions below in the questions section.\n\n9) Please do not connect the dots in Figure 4 as the x-axis represents distinct categories rather than a continuous scale.\n\n10) It should be stated if the 40GB or 80GB version of A100s was used.", "was_revised": true, "final_justification": "The authors rebuttal has addressed all my concerns and questions, especially regarding the test sets the approaches were evaluated on.", "rebuttal": "We appreciate your recognition of writing and easy-to-follow. We will answer the questions below, and we hope this clears up your concerns.\n\n**Q1:** I would recommend to present the main motivation in the abstract and introduction differently.\n\n**A1:** We agree that structured tables (e.g., in databases, CSVs) are ubiquitous and form the backbone of many data systems. Our intention was **not to** diminish their importance, **but to** highlight a specific, challenging, and highly practical set of scenarios where table images are easier to obtain rather than structured tables.\n\nWe think that structured tables are often hard to get at some cost. For example, \n\n- In **scanned legacy documents** like financial reports, invoices, scientific papers, and insurance claims, tables are most often available as PDFs or images.\n- It is hard to extract data from **screenshots** of web pages or proprietary software applications where API access is unavailable.\n- Even with OCR methods, the extracted tables often contain some noise.\n\nIn these widespread scenarios, the inability to programmatically access the underlying structured data represents a major bottleneck for automation and data analysis. Our work is specifically designed to tackle this \"last mile\" problem of data extraction and reasoning. The core premise of TURBO is that while *at inference time* we only have these challenging table images, *during the training phase*, it is often feasible to obtain the original structured source data. This real-world asymmetry is precisely what makes the privileged information learning paradigm so suitable and powerful for this problem.\n\nWe thank you for the valuable suggestions. We will refine the introduction to clearly articulate that we are addressing the challenge of reasoning over visually presented tables, which is a prevalent and unsolved problem in many document-centric workflows, rather than making a claim about all tables in general. We believe these changes will make our motivation clearer, more accurate and will ultimately provide a stronger foundation for our work.\n\n**Q2:**  The terms \"bridged information\" and \"privileged structured information\" should be explained in the introduction.\n\n**A2:** Here are the definitions we will integrate into the introduction of the revised manuscript, likely right after these terms are first introduced:\n\n- **Privileged Structured Information:** In the context of our work, this refers to the **clean, structured representation of a table (e.g., in Markdown format)**. It is considered \"privileged\" because we assume it is accessible *only during the training phase* to guide and supervise our model. At inference time, this information is *unavailable*, and the model must perform its reasoning based solely on the table image. \n- **Bridged Information:** This is the term we use for the high-quality data we generate to connect the two modalities (structured text and table images). Specifically, \"bridged information\" refers to the **modality-agnostic [question, reasoning trace, answer] triplets** that we create using our structure-aware generator. The core of this is the reasoning trace, which is derived from the privileged structured table. We call it \"bridged\" because it serves as a **conceptual bridge**, allowing us to transfer the logical reasoning steps learned from the clean, structured domain to the visually-complex image domain, effectively bridging the modality gap.\n\n We thank the reviewer for helping us improve the accessibility of our paper and we will make this clear in the final version.\n\n**Q3:** Other suggestions in weaknesses.\n\n**A3:** We are sincerely grateful to the reviewer for their thorough and meticulous review of our manuscript. We agree with all the points raised and commit to implementing the corresponding changes in the final version.\n\nBelow, we address each point individually to confirm our understanding and outline the specific revisions we will make, including the comparison with HIPPO, the clarification of Figure 1 experimental setup, the claim in line 166, the data visualization in Figure 4, and the  80GB version of A100s we used.\n\nWe appreciate the opportunity to clarify that the case in Figure 2 was not chosen arbitrarily. It was **randomly selected from the official MMMU benchmark**, from a subset of cases that are explicitly **tagged as 'table'-related questions** by the benchmark's creators. Our motivation for including this particular case was to demonstrate that real-world and benchmark challenges for \"tabular reasoning\" often encompass more than just simple, perfectly structured grids. \n\n\n**Q4:** The paper and appendix describe the training set of 9k datapoints (a mix from several different dataset), but no details about the test set are mentioned. Based on which data are the experiment results (Table 1, Figure 4) reported?\n\n**A4:** We sincerely thank the reviewer for this crucial question. We apologize that this was not made sufficiently clear in the initial submission. We would like to explicitly clarify our evaluation protocol.\n\n**The results reported in Table 1 and Figure 4 are evaluated on the official, held-out test splits for each respective benchmark (TABMWP, WTQ, HiTab, TAT-QA, TabFact, and InfoTabs).**\n\nTo elaborate on our process, which follows the setting in HIPPO:\n\n1. Our 9k training set was constructed by randomly sampling instances **exclusively from the official training splits** of the source datasets.\n2. We have taken care to ensure a strict separation. **No data from any of the official test splits was ever used or seen during any phase of our model's training**, including the initial SFT and the subsequent RL stage. There is no risk of data leakage. The model is evaluated on data it has never been exposed to.\n3. Furthermore, to evaluate the generalization capabilities of TURBO on completely unseen data distributions and formats, we included the challenging **MMMU benchmark** as an additional test set. The strong performance TURBO achieves on MMMU therefore demonstrates robust zero-shot generalization capabilities, which is a key strength of our framework.\n\nWe will update the **\"Evaluation Benchmarks\" paragraph (Section 5.1)** in the final manuscript to explicitly state these details.\n\n**Q5:** What also did not get clear to me: In your experiments, is each model (also the ones compared to) fine-tuned on the 9k datapoints from the training set? Please provide details on the exact setup.\n\n**A5:** We thank the reviewer for this critical question. Our TURBO models were, of course, trained on this dataset. The other baselines were evaluated under the most appropriate and common protocols for each, which we detail below. We designed our experiments to provide three distinct levels of comparison, ensuring a comprehensive and fair evaluation.\n\n1. **A Direct and Fair Comparison (vs. HIPPO):**\n   Our primary comparison for fairness is against **HIPPO**, a state-of-the-art model for tabular reasoning. To ensure the most direct and equitable comparison possible, we **explicitly adopted HIPPO's data sampling strategy**, using the same source datasets and a similar number of training instances. This setup isolates the methodological differences between TURBO and HIPPO, making it a fair and direct comparison of the two approaches.\n2. **A \"Stress Test\" Against General SOTA Models (vs. Qwen, InternVL, MiniCPM-V):**\n   For large, general-purpose MLLMs like Qwen2.5-VL, we evaluate their **zero-shot performance** \"off-the-shelf.\" This is standard practice in the field for several reasons:\n   - **Practicality:** Fine-tuning every large-scale baseline model is computationally hard and, in many cases, impractical without access to their specific training hyperparameters.\n   - **Relevance:** This comparison answers a critical question for any practitioner: \"Is it better to use this new, specialized model, or can I get similar performance from a powerful, general model out of the box?\" Our results demonstrate that for this specific task, our specialized training provides a significant advantage.\n   - **Data Purity:** These foundation models are trained on massive, web-scale datasets, and we cannot verify whether their training sets inadvertently contained instances from our benchmarks' test sets. Evaluating them zero-shot is the most established community norm for comparison.\n3. **A Controlled Internal Comparison (Ablation Study, Figure 4):**\n   Finally, our most controlled experiment is the **ablation study**. By comparing our baseline model (Ovis2) with the same model after applying our SFT and RL stages (Ovis2 + TURBO), we can **precisely measure the performance gain attributable solely to our TURBO framework**. The significant improvements shown in Figure 4 demonstrate that our methodology provides substantial gains, independent of the baseline comparison.\n\nWe will add this detailed explanation to the experimental setup section to ensure full transparency for the reader. \n\n**Q6:** In Table 4 (appendix): What does the \"Baseline-textonly\" refer to?\n\n**A6:** We thank the reviewer for this question, which allows us to clarify an important aspect of our analysis in the appendix.\n\nThe term **\"Baseline-textonly\"** refers to our base model, **Ovis2**, when it is evaluated in a **text-only setting**.\n\nSpecifically, for this experiment, instead of providing the model with a table image, we provide it with the **structured Markdown table as its sole input**. MLLMs like Ovis2 are perfectly capable of processing text-only inputs, so this allows us to measure the model's inherent text-based reasoning capabilities before any of our specialized training is applied.\n\nBy comparing the performance of \"Baseline-textonly\" against \"TURBO-textonly,\" we can isolate and demonstrate a key strength of our framework: **The reasoning skills learned through our TURBO pipeline are not just visual, they also enhance the model's fundamental, modality-agnostic reasoning abilities.**"}, {"review_id": "U4pJgFNpI9", "reviewer": "Reviewer_AKQf", "summary": "This paper introduces TURBO, a framework for multimodal tabular reasoning that leverages privileged structured table information during training to enhance reasoning over table images. The key challenge addressed is aligning structured data with visual representations and transferring reasoning skills across modalities. TURBO uses a structure-aware reasoning trace generator based on DeepSeek-R1 to create high-quality [question, reasoning, answer] triples, followed by supervised fine-tuning (SFT) and reinforcement learning (RL) with Group Relative Policy Optimization (GRPO) to optimize reasoning paths iteratively.", "questions": "- How does TURBO perform on tables with merged cells, multi-level headers, or irregular layouts (e.g., HiTab)? Are there plans to address such complexities?\n\n- How sensitive is TURBO to the quality of structured tables used during training? Would noisy or incomplete structured data degrade performance?\n\n- Does the two-stage training affect inference-time efficiency? How does TURBO compare to baselines in terms of reasoning speed?", "limitations": "yes", "rating": 4, "confidence": 1, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths:\n\n- TURBO innovatively uses structured tables as privileged information to guide reasoning over table images, addressing a critical real-world challenge where structured data is often unavailable at inference time.\n\n- The structure-aware reasoning trace generator produces high-quality supervision signals, filtering redundant content and emphasizing relevant logical steps, which significantly improves model performance.\n\n- The combination of SFT and RL (GRPO) enhances both initial reasoning capabilities and iterative optimization of reasoning paths, leading to robust and interpretable results.\n\n- TURBO demonstrates significant improvements across diverse benchmarks (e.g., TABMWP, WTQ, MMMU) with limited data, outperforming prior SOTA methods and showcasing generalization to complex table layouts.\n\nWeaknesses:\n\n- Experiments primarily focus on regular tables, while real-world tables with merged cells or nested headers (e.g., HiTab) are underrepresented, potentially limiting applicability to highly complex layouts.\n\n- The framework relies on structured tables during training, which may not always be available in practice, though the paper acknowledges this as a limitation.\n\n- The GRPO-based RL stage requires extensive training (24 hours with 8 A100 GPUs), which could be resource-intensive for smaller research teams.", "was_revised": true, "final_justification": "I would retain my positive score at this stage", "rebuttal": "We appreciate your recognition of innovation, high-quality supervision signals, robust and interpretable results and significant improvements. We will answer the questions below, and we hope this clears up your concerns.\n\n**Q1:** Experiments primarily focus on regular tables, while real-world tables with merged cells or nested headers (e.g., HiTab) are underrepresented.\n\n**A1:** We sincerely thank the reviewer for raising these critical points regarding the complexity of table structures and the practical availability of training data. We would like to clarify that our evaluation was intentionally designed to include benchmarks with complex, non-regular table structures to ensure our framework is not limited to simple grid-like tables.\n\n1. **Evaluation on HiTab:** We explicitly included the **HiTab dataset** in our main experiments (Table 1). HiTab is specifically known for its challenging hierarchical tables, which feature nested headers, merged cells, and complex layouts that require a deep understanding of table structure. Our TURBO model achieves a score of **72.15% on HiTab**, demonstrating a clear performance advantage over strong baselines like Qwen2.5-VL and showcasing its effectiveness on these complex structures.\n2. **Evaluation on Diverse, Real-World Tables (MMMU):** To further test the robustness and generalization of our method on a wide variety of \"in-the-wild\" tables, we evaluated TURBO on the **MMMU benchmark**. The table-related questions in MMMU are sourced from diverse, real-world documents and often feature irregular layouts and visual noise, making it an excellent proxy for real-world applicability. On this challenging benchmark, TURBO achieves an accuracy of **57.32%**, which represents a **significant +13% relative improvement over the next best method**.\n\nThe strong performance on both HiTab and MMMU provides direct evidence that our TURBO framework is indeed capable of handling the complex and varied table layouts encountered in real-world scenarios.\n\n**Q2:** The framework relies on structured tables during training, which may not always be available in practice.\n\n**A2:** We acknowledge that the requirement of having access to structured tables during the training phase defines the specific problem domain our work addresses. We frame this not as a universal solution, but as a powerful methodology for a very common and practical set of real-world problems where a **natural asymmetry** exists between training and deployment.\n\nThis scenario is prevalent in many automated document processing pipelines, such as:\n\n- Extracting information from **financial or scientific reports**, which are often generated from structured sources but are consumed as PDFs.\n- Reasoning over **webpages**, where the underlying HTML is available during development but inference must happen on screenshots.\n\nOur core contribution is to provide an effective solution for these specific, high-value scenarios by leveraging the \"privileged\" structured data when it is available to build a far more capable visual reasoning model for deployment. We will ensure this problem framing is made even clearer in the final version of the paper.\n\n**Q3:** The GRPO-based RL stage requires extensive training, which could be resource-intensive for smaller research teams.\n\n**A3:** We sincerely thank the reviewer for raising this. In fact, one of the primary motivations behind our two-stage design was to **maximize performance gains while being mindful of the high cost during training**. \n\n1. **The Highly Efficient SFT Stage Delivers the Majority of the Gains:**\n   The core of our TURBO framework's effectiveness comes from the first SFT stage. This stage is exceptionally resource-efficient, requiring only **1 hour of training on 4 A100 GPUs**, a level of compute that is widely accessible.\n\n   Crucially, as demonstrated in our ablation study, this highly efficient SFT stage is responsible for the vast majority of the performance improvement. It boosts the average performance of the Ovis2 model from a baseline with SFT. This represents a **~10% absolute improvement** achieved at a minimal computational cost. This means that research teams can adopt the most impactful part of our methodology without requiring extensive resources.\n\n2. **The RL Stage as an Optional Optimization for Peak Performance:**\n   The subsequent RL stage, which does require a more significant investment (24 hours on 8 A100s), should be viewed as an **optional, intensive fine-tuning step** for teams seeking to extract the maximum possible performance and push the state-of-the-art. This stage further refines the model's reasoning capabilities, providing an additional gain to reach the final reported SOTA performance of 76.42%.\n\nIn summary, our TURBO framework is designed to be modular and scalable. **The core contribution and the majority of the performance uplift can be achieved with the highly economical SFT stage**, making our method accessible to a broad range of research teams.\n\n**Q4:** How sensitive is TURBO to the quality of structured tables used during training? Would noisy or incomplete structured data degrade performance?\n\n**A4:** We sincerely thank the reviewer for this insightful question. The reviewer is correct: **TURBO's performance is highly sensitive to the quality of the structured reasoning traces used during training, and we have direct evidence of this from our development process.**\n\nIn our initial experiments, we generated the reasoning traces by prompting a powerful LLM (DeepSeek-R1) with the question and the structured table. However, we observed a significant challenge: in a non-trivial number of cases, the final answer derived from the LLM's generated reasoning trace did **not** match the ground-truth answer provided in the dataset.\n\nTraining our model on these \"incoherent\" data triplets introduced significant noise into the learning process. This effectively confused the model, asking it to learn a reasoning process that contradicted the target outcome. As a result, the performance of the model trained on this initial, noisy data was substantially lower.\n\nTo address this critical issue, we introduced the **Reject Sampling** step (in lines 205) as a core component of our data generation pipeline. This step acts as an essential quality filter. We only retain the generated [question, reasoning_trace, answer] triplets where the answer part of the LLM's generation is **fully consistent** with the ground-truth label.\n\nBy implementing this reject sampling mechanism, we filtered out the noisy, contradictory examples and created a much cleaner, higher-fidelity training dataset of 9k instances. **Training our model on this cleaned dataset resulted in a significant and consistent performance improvement across all benchmarks compared to our initial experiments without reject sampling.**\n\nThis experience directly confirms that TURBO's performance is indeed sensitive to the quality of its privileged supervision. More importantly, it validates the necessity and effectiveness of the reject sampling mechanism, which is a crucial, non-trivial component of our framework designed to ensure robustness and maximize learning efficiency from the generated data.\n\nWe will add this detailed discussion to the paper to provide a clearer insight into our methodology and design choices. We thank the reviewer for prompting this important clarification.\n\n**Q5:** Does the two-stage training affect inference-time efficiency? How does TURBO compare to baselines in terms of reasoning speed?\n\n**A5:** We sincerely thank the reviewer for this excellent and practical question. Inference efficiency is indeed a critical aspect of any deployable model. We have analyzed the inference speed of TURBO and can provide the following clarification.\n\nOur two-stage training process (SFT + RL) **only updates the weights of the model; it does not alter the model's architecture or increase its parameter count**. Therefore, the fundamental per-token generation speed of our TURBO model is identical to that of the original Ovis2 baseline. The model itself is not inherently slower.\n\nThe observable increase in total inference time for TURBO comes from a deliberate design choice: the model generates a longer output sequence. Instead of producing only a final answer, TURBO generates a full reasoning trace. This naturally takes more time because more tokens need to be generated.\n\nTo provide concrete data, we measured the average inference time and output length on the WTQ dataset:\n\n| Model | Avg. Inference Time per Sample (A100-80G) |\n| - |- |\n| Ovis2 (Baseline) | ~0.8 seconds |\n| TURBO | ~2.7 seconds   |\n| QvQ-72B | ~108 seconds |\n\nThis data highlights a clear trade-off: TURBO's inference time is longer than its own baseline's because it's performing and articulating a complex reasoning process. This is the cost of achieving significantly higher accuracy and providing a traceable output for error analysis.\n\n**Competitive Speed Compared to Other SOTA Models:**\nCrucially, when we compare TURBO's inference speed to that of other powerful, state-of-the-art multimodal models like **QvQ**, we find that **our inference time is not an outlier and remains highly competitive**. Despite generating a detailed reasoning trace, TURBO's speed is well within the acceptable limits for models in this performance class. This demonstrates that the efficiency cost for our significant accuracy gains is reasonable and practical.\n\nIn summary, the two-stage training does not slow down the model's intrinsic speed. The longer inference time is a direct and predictable consequence of generating more detailed, reasoned outputs. This represents a favorable trade-off for achieving state-of-the-art accuracy, and the resulting speed is comparable to other leading models. We will add this analysis to the final version of the paper to provide a complete picture of our method's performance characteristics."}]

Author rebuttal:

Meta-review: The paper introduces TURBO, a framework for multimodal tabular reasoning that leverages privileged structured information during training to improve reasoning over table images. By combining a structure-aware reasoning trace generator, supervised fine-tuning, and reinforcement learning, TURBO achieves strong performance across multiple benchmarks and demonstrates high parameter efficiency. While reviewers initially raised concerns about originality, baseline comparisons, and limitations, the rebuttal addressed these thoroughly with new experiments, clearer motivation, and detailed failure analyses. Given its solid methodology, scalability, and state-of-the-art results, I recommend acceptance.
Decision: Accept (poster)