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REVIEW 5 major objections 4 minor 21 cited by

MAmmoTH-VL: Eliciting Multimodal Reasoning with Instruction Tuning at Scale

T0 review · 5 major / 4 minor · reviewed 2026-08-11 · deepseek-v4-flash

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Pith's one-line read The paper argues that an open, scalable pipeline of rewriting existing visual instruction data into chain-of-thought rationales and self-filtering the result can push an 8B multimodal model to top open-source benchmark results.

desk verdict Useful open data pipeline and strong 8B model, but the headline benchmark gains are not decontaminated; the MathVerse claim in particular can't be trusted until overlap is checked. read the letter →

arxiv 2412.05237 v2 pith:C5JEH547 submitted 2024-12-06 cs.CL cs.CV

Jarvis Guo , Tuney Zheng , Yuelin Bai , Bo Li , Yubo Wang , King Zhu , Yizhi Li , Graham Neubig
show 2 more authors
Wenhu Chen Xiang Yue
This is my paper · ORCID
classification cs.CLcs.CV
keywords multimodallargelanguagemodelsinstructiontuningchain-of-thoughtreasoningdatarewritingself-filteringopen-sourcevisualdatasetscaling
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

The paper is trying to establish that the main bottleneck in open multimodal reasoning is instruction data, not architecture, and that a cheap, fully open data pipeline can remove it. The authors collect 153 public instruction datasets, rewrite 60 of them into 12 million question-answer pairs with detailed step-by-step rationales using open-weight models, and filter the result with the same model acting as judge. Training an 8B model on this MAmmoTH-VL-Instruct data yields reported gains of 8.1 points on MathVerse, 7 points on MMMU-Pro, and 13.3 points on MuirBench over prior open-source models, plus up to 4 points on non-reasoning benchmarks. If the results hold, high-quality reasoning data becomes reproducible without proprietary APIs, and dataset construction itself becomes a scalable lever for model improvement.

What carries the argument

The load-bearing machinery is the rewrite-and-filter pipeline rather than any single new architecture. First, manual screening assigns each of 153 datasets to Group A (kept as is), Group B (rewritten), or Group C (discarded). Second, task-specific prompts drive InternVL2-Llama3-76B to rewrite Group B image tasks into complex instructions with detailed rationales, while Llama-3-70B-Instruct rewrites caption data using text only. Third, the same InternVL2 model scores each rewritten pair with a binary Yes/No judgement on relevance, consistency, and factual accuracy, removing roughly half of OCR and chart data where hallucinations are most common. Training then mixes 70% rewritten with 30% original data and scales the mixture from 2M to 12M samples across ten categories.

What would settle it

Take a random sample of accepted OCR and chart pairs from the 12M dataset, have independent human annotators mark each answer as factually correct or not, and compute the false-acceptance rate of the model judge; if a substantial share of accepted pairs contain errors that humans catch, the self-filtering step does not remove hallucinations and the reasoning gains need another explanation.

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Extended reading notes

Core claim

The central claim is that simple rewriting plus self-filtering, applied at 12-million-sample scale, converts phrase-level visual question answering data into chain-of-thought reasoning data, and that an 8B model trained on it reaches top results among open-source models on reasoning-heavy benchmarks. The authors would state this as: our three-step pipeline, curating 153 sources into keep, rewrite, and drop groups, rewriting task-aware prompts with InternVL2-Llama3-76B and Llama-3-70B-Instruct, then filtering hallucinated or inconsistent pairs with the same InternVL2 model as judge, produces a dataset whose faithfulness and diversity let an 8B LLaVA-OneVision model beat much larger open models. Ablations show that filtering matters most for chart and document understanding, that mixing 70% rewritten with 30% original data helps, and that performance improves steadily as training data grows from 2M to 12M samples.

Load-bearing premise

The load-bearing premise is that the open model that generated the rewritten answers, InternVL2-Llama3-76B, is also trustworthy enough to judge whether those answers are correct, so any blind spot the model has in reading images can quietly become the training set's standard.

Editorial extensions

If this is right

  • Open-source teams can build large reasoning-oriented multimodal datasets without paying for proprietary generation or human annotation at scale.
  • Ablations indicate that filtering is a necessary component: removing it hurts performance most on chart and document understanding, where hallucination rates are highest.
  • Data scale behaves like a reliable lever: gains track training-set size from 2M to 12M samples across nearly all reported benchmarks.
  • Rewrite-model size matters unevenly: 7B-scale rewrites match 76B-scale rewrites on knowledge and reasoning tasks, but larger models are needed for chart, document, and preference-style data.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • A direct way to test the generalization claim is to evaluate MAmmoTH-VL-8B on reasoning benchmarks released after this dataset was assembled; if the edge shrinks, part of the reported gain may come from overlap with training sources.
  • Because the judge is the same model that wrote the answers, systematic blind spots could let confident hallucinations pass; an independent verification model or a targeted human audit of OCR and chart subsets would quantify how much filtering actually buys.
  • The observed parity between 7B and 76B rewrite models on knowledge tasks suggests the recipe may be cheap enough to reuse for video, audio, or non-English modalities where data is scarcer.
  • A practical extension would insert an external OCR or chart-parsing tool into the rewrite step, since those categories show the highest filter rates and the largest measured gains from filtering.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

5 major / 4 minor

Summary. The paper introduces MAmmoTH-VL-Instruct, a 12M-pair multimodal instruction-tuning dataset constructed by rewriting 153 open instruction datasets with open-weight models (InternVL2-Llama3-76B and Llama-3-70B-Instruct) and then filtering the rewritten data with the same InternVL2 model acting as a judge. The authors train MAmmoTH-VL-8B, a LLaVA-OneVision-style model with a Qwen2.5-7B language backbone, and report state-of-the-art open-source results on MathVerse (+8.1%), MMMU-Pro (+7%), and MuirBench (+13.3%), along with gains on chart and document benchmarks. Ablations with a 1.5B backbone examine filtering, rewritten/original mixing ratio, training-data scale, and rewrite-model size.

Significance. If the reported gains are clean, the paper makes a useful community contribution: it demonstrates a fully open pipeline for producing rationale-rich multimodal instruction data at scale, releases the resulting dataset, and provides detailed prompts and scaling curves. The use of open models only, the careful categorization of 153 sources, and the public evaluation protocol via LMMs-Eval are all strengths. However, the central empirical claim is currently not cleanly established because several evaluation benchmarks overlap with training sources, the self-filtering evaluation is partly circular, and some reported deltas are not reproducible from the paper's own tables. These issues are fixable and do not invalidate the dataset contribution, but they must be resolved before the performance claims can be accepted.

major comments (5)
  1. [Section 2.1, Figures 3/A3/A4, Tables 2-4] The evaluation is contaminated by training-source overlap. ChartQA, DocVQA, Infographic-VQA, and WildVision appear in the training mix (Figure A4 and Figure A3), and Group A includes Video-MME and EgoSchema, which are then used as evaluation benchmarks in Table 4. MathVerse is assembled from existing visual-math problems, and several of its source collections (Geometry3K, GeoQA+, TabMWP, UniGeo, CLEVR-Math, MapQA, IconQA, MathVision) appear in the Group B rewrite inputs via MathV360K. No decontamination analysis is reported anywhere in the paper. As a result, the headline deltas (MathVerse +8.1, MMMU-Pro +7, MuirBench +13.3) cannot be attributed cleanly to the rewriting pipeline. Please report exact and near-duplicate overlap statistics for every evaluation benchmark, and either retrain without overlapping sources or explicitly quantify the effect of overlap on the reported gains.
  2. [Section 4.1 and Table A2] The reported dataset size is internally inconsistent. Table A2 lists pre-filter counts summing to about 12.3M and post-filter counts to about 7.8M, while the paper claims a 12M final dataset and Section 4.1 trains Stage-2 on 10M single-image pairs. The per-category proportions in Figure 3 also do not match Table A2's per-category counts (e.g., Chart is 15.4% of 12M in Figure 3 but 7.3M pre-filter or 3.8M post-filter in Table A2). Please clarify whether Table A2 refers to the entire corpus, the Group B rewritten subset, or a sample; without this, the dataset-size and scaling claims in Figure 1 cannot be reproduced.
  3. [Sections 2.3, 3.1, 3.3] The self-filtering and quality verification use the same model (InternVL2-Llama3-76B) both to generate and to judge the data. The only human-agreement check is on 60 items, and Figure 4's content and relevance scores are assigned by the same model that produced the rewrites. This leaves open the possibility that systematic errors of the generator are shared by the judge, so the quality demonstration is partly self-confirming. Please add an independent judge (a different large MLLM or human annotation on a substantially larger sample) and report agreement separately for content and relevance, and per data category.
  4. [Tables 2, 3, 4 (delta rows)] The 'Over Best Open-Source (~10B Scale)' deltas are not reproducible from the numbers in the same tables. For example, in Table 2, Qwen2-VL-7B scores 26.9 on MMMU-Pro vision while MAmmoTH-VL-8B scores 25.3, yet the delta row is +7.1; in Table 3, Qwen2-VL-7B scores 76.5 on InfoVQA while MAmmoTH-VL-8B scores 73.1, yet the delta row is +2.2; and in Table 4, InternVL2-8B scores 59.4 on MuirBench, above MAmmoTH-VL-8B's 55.1, despite the +13.3 delta. Please specify the exact comparison set (which models are considered 'open-source' and what '~10B scale' means) and recompute the deltas; the current presentation overstates the state-of-the-art claims.
  5. [Section 5 and Appendix A.1] All ablations use Qwen2.5-1.5B-Instruct as the backbone and report single runs without variance estimates, while the main model is 8B. The claims that self-filtering, rewriting, and the mixing ratio are crucial for the 8B results are therefore extrapolations from a much smaller model. Please either run the key ablations (filtering and rewriting ratio) at the 8B scale or add variance estimates and explicitly discuss the transfer limitations; without this, the component importance claims are not directly supported for the submitted model.
minor comments (4)
  1. [Figure 4] The left panel plots content and relevance scores on a common axis but does not define how the two metrics are aggregated or whether the displayed value is a mean over samples; a legend or separate panels would improve clarity.
  2. [Section 3.3 and Table A5] The Kappa analysis is based on only 60 items and no confidence intervals are reported; given the small sample, the difference between 0.55 and 0.64 should be interpreted cautiously rather than as strong evidence that the model agrees with humans better than humans agree with each other.
  3. [Table A2 caption] The caption should state whether the counts are for the full corpus, the Group B rewritten subset, or a sample; as written, the table conflicts with the 'randomly sample 1M instances' description in Section A.1.
  4. [References] The reference list contains duplicate entries (e.g., ChartQA, DocVQA, UReader) and some entries lack venue information; please clean up the bibliography.

Circularity Check

2 steps flagged · score 5.0 of 10

Benchmark gains are partly self-referential: training mix overlaps the evaluated benchmarks and the rewriting-quality check uses the same generator as judge.

  1. fitted input called prediction [Section 4.3 (Tables 2-3) vs Figure 3 and Appendix Figure A4]
    "Chart (15.4%) mPLUG-DocOwlchart Ureader Chart Ureader QA DVQA ArXiv-Chart-GPT4o PlotQA ArxivQA InfographicVQA Robut-WTQ Robut-SQA Hitab TAT-QA FinQA Vistext ChartQA Robut-WikiSQL Ureader KG Chart2Text Irv-Chart"

    Figure 3 lists ChartQA as a training source, and Table 3 reports ChartQA test accuracy after training on samples from this 12M mixture; no decontamination is reported. The same holds for DocVQA, InfoVQA, and AI2D. For MathVerse, Figure A4 Group B contains Geometry3K, GeoQA+, TabMWP, UniGeo, CLEVR-Math, MapQA, IconQA, and MathVision, which are the source collections from which MathVerse is assembled. Thus the headline gains, especially MathVerse +8.1%, are partly forced by train/eval overlap rather than being clean out-of-distribution evidence for CoT rewriting.

  2. other [Sections 2.3 and 3.1 (Figure 4)]
    "Specifically, we leverage the InternVL2-Llama3-76B model, the same model used during the data rewriting process, to evaluate the logical consistency of each question-answer pair against the corresponding image."

    The quality verification that 'the rewritten dataset consistently surpasses the original dataset' (Figure 4) scores both original and rewritten data with InternVL2-Llama3-76B, the same model that generated the rewritten responses. Higher scores for rewritten data can therefore reflect self-preference rather than independent quality, so the quality comparison is self-confirming. The small 60-item human Kappa check (Section 3.3) gives partial external grounding, but the main quality claim still rests on a generator-as-judge loop.

full rationale

MAmmoTH-VL is primarily an empirical dataset-construction paper, not a formal derivation, so most of the pipeline is not circular: rewriting, filtering, and ablations are ultimately checked by downstream benchmarks, and the self-filtering step is partly validated against human ratings. However, two steps make part of the evaluation self-referential. First, the training inventory includes the benchmark datasets or their source collections: ChartQA, DocVQA, InfoVQA, and AI2D appear in the training mix, and the MathVerse source collections (Geometry3K, GeoQA+, TabMWP, UniGeo, CLEVR-Math, MapQA, IconQA, MathVision) appear in Appendix Figure A4; with no decontamination reported, the reported benchmark gains are not fully external. Second, the quality comparison of original vs rewritten data uses the same InternVL2-Llama3-76B model as both generator and judge, making the quality claim at least partly self-confirming. These issues are partial rather than total: MMMU-Pro and MuirBench are not shown to overlap with training, and the human Kappa check provides some independent grounding. The score of 5 reflects one headline result that is statistically forced by train/eval overlap plus a self-referential quality check, while the central dataset-construction claim still retains independent content.

Assumptions & free parameters 3 free parameters · 4 assumptions · 0 invented entities

No new physical or mathematical entities are introduced. The free parameters are data-engineering choices such as mixing ratio and training sample sizes rather than fitted constants. The main burden is the domain assumption that the generating model can also judge its own outputs.

free parameters (3)
  • rewritten_data_ratio = 0.7 (70% rewritten, 30% original)
    Chosen as a balanced choice after ablation across ratios 0, 0.3, 0.5, 0.7, and 1.0; see Section 5.2 and Table A3.
  • stage2_training_size = 10M samples
    Randomly sampled from the 12M dataset for the single-image instruction-tuning stage, with no stated criterion for the sample.
  • data_scale_intervals = 2M increments
    The scaling curves use 2M, 4M, 6M, 8M, 10M, and 12M points; the interval size is an arbitrary choice for visualization, not derived from theory.
assumptions (4)
  • domain assumption The same open MLLM used to rewrite data can serve as a reliable judge of its own outputs.
    Sections 2.3 and 3.1 rely on model-as-judge for filtering and quality scoring without independent verification beyond a 60-item Kappa check.
  • domain assumption Existing academic VQA datasets provide sufficient raw material for rewriting into diverse reasoning tasks.
    Section 2.1 assumes the 153 collected datasets cover the desired task distribution after rewriting.
  • domain assumption The LLaVA-OneVision training stages are a suitable base for demonstrating data quality.
    Section 4.1 adopts the LLaVA-OneVision recipe without ablating architecture choices, so the reported gains are only about data, not model design.
  • domain assumption A Yes/No filtering prompt removes hallucinations without discarding useful examples.
    The filtering step in Section 2.3 assumes binary judgments are sufficient to separate good from bad rationales at scale.

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Cite this review

Pith. "Pith review of MAmmoTH-VL: Eliciting Multimodal Reasoning with Instruction Tuning at Scale." pith.science (2026). https://pith.science/paper/C5JEH547

@misc{pith2026241205237,
  author       = {Pith},
  title        = {Pith review of: MAmmoTH-VL: Eliciting Multimodal Reasoning with Instruction Tuning at Scale},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/C5JEH547}},
  note         = {Machine review of arXiv:2412.05237}
}
read the original abstract

Open-source multimodal large language models (MLLMs) have shown significant potential in a broad range of multimodal tasks. However, their reasoning capabilities remain constrained by existing instruction-tuning datasets, which were predominately repurposed from academic datasets such as VQA, AI2D, and ChartQA. These datasets target simplistic tasks, and only provide phrase-level answers without any intermediate rationales. To address these challenges, we introduce a scalable and cost-effective method to construct a large-scale multimodal instruction-tuning dataset with rich intermediate rationales designed to elicit CoT reasoning. Using only open models, we create a dataset containing 12M instruction-response pairs to cover diverse, reasoning-intensive tasks with detailed and faithful rationales. Experiments demonstrate that training MLLMs on this dataset significantly improves reasoning capabilities, achieving state-of-the-art performance on benchmarks such as MathVerse (+8.1%), MMMU-Pro (+7%), and MuirBench (+13.3%). Additionally, the model demonstrates notable improvements of up to 4% on non-reasoning-based benchmarks. Ablation studies further highlight the importance of key components, such as rewriting and self-filtering, in the dataset construction process.

Figures

Figures reproduced from arXiv: 2412.05237 by the authors.

Figure 1
Figure 1. Scaling effects of MAmmoTH-VL-8B on eight multimodal evaluation datasets. A [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Overview of our simple yet scalable visual instruction data rewriting pipeline with three steps: manual data source collection, rewriting using MLLMs/LLMs, and filtering via the same MLLM as a judge. Examples below illustrate transformations in math and science categories, showcasing detailed, step-by-step responses. repurposed from academic VQA datasets (Antol et al., 2015; Singh et al., 2019), which focus on phras… view at source ↗
Figure 3
Figure 3. The data distribution of MAmmoTH-VL-Instruct (12M). Left: Category distribution. Right: Details of data sources. 2 Method While previous efforts have highlighted the potential of visual instruction tuning, many rely on resource￾intensive methods such as human annotations or propri￾etary models. These approaches limit scalability and accessibility, particularly in open-source contexts. To address these challenges, we… view at source ↗
Figures from the paper (5 more)
Figure 4
Figure 4. Figure 4: Comparison of original and rewritten data [PITH_FULL_IMAGE:figures/full_fig_p004_4.png]
Figure 6
Figure 6. Figure 6: The filter rates of different data types after [PITH_FULL_IMAGE:figures/full_fig_p005_6.png]
Figure 7
Figure 7. Figure 7: Data filtering significantly improves the qual [PITH_FULL_IMAGE:figures/full_fig_p008_7.png]
Figure 8
Figure 8. Figure 8: Effect of the mixing ratio of rewritten and [PITH_FULL_IMAGE:figures/full_fig_p008_8.png]
Figure 9
Figure 9. Figure 9: Performance of data rewritten by different models on three benchmark subsets [PITH_FULL_IMAGE:figures/full_fig_p009_9.png]

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    - Your output should only consist of the generated <instruction, response>pairs

    Output Clarification: - Do not include additional text or explanations outside of the required <instruction, response>pairs. - Your output should only consist of the generated <instruction, response>pairs. ### Example: Caption: a street scene with construction scaffolding, thr...

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    Vary the types of questions and tasks (e.g., open-ended questions, specific queries, instructions)

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    Maintain a natural conversational flow

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    Avoid directly stating key visual elements in the questions

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    **Consistency**: Verify if the answer is logically coherent and aligns with the question

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    Super VGA Graphics

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    Northgate Graphics Card

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    OS/2 READY!

    OS/2 READY: The image indeed has an "OS/2 READY!" label, which is correct. However, this only indicates system compatibility with OS/2 and does not necessarily relate to high-performance graphical capabilities

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    101-key Click-Tactile Enhanced Keyboard,

    101-key Enhanced Keyboard: The image indeed mentions that the system comes with a "101-key Click-Tactile Enhanced Keyboard," and this description is accurate

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    This mismatch leads to an incorrect average

    Calculation Error for Averages: - For the U.S., the values in the graph are 73, 57, 49, and 64, but the calculation uses 73, 54, 49, and 64. This mismatch leads to an incorrect average. The actual average for the U.S. should be (73 + 57 + 49 + 64)/4 = 243/4 = 60.75%. - For Ger...

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    average is 60% and Germany’s average is 61.75%, but the recalculated averages (60.75% for the U.S

    Incorrect Comparison: - The answer claims that the U.S. average is 60% and Germany’s average is 61.75%, but the recalculated averages (60.75% for the U.S. and 60.25% for Germany) reveal the opposite trend. The U.S. has a slightly higher average, not Germany

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    more consistent and stable support for NATO

    Misinterpretation of Significance: - The conclusion states that Germany has "more consistent and stable support for NATO" compared to the U.S., based on the incorrect averages. However, the actual data shows that the averages are nearly the same, and there is no significant ev...

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    For example, U.S

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    and 61.75% for Germany) without clarifying that this is an approximation, which can lead to a misrepresentation of the actual differences

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    - The sum of the angles in this triangle is also 180◦

    Next, consider the triangle on the right with angles 68◦, ∠2, and ∠3. - The sum of the angles in this triangle is also 180◦. - Therefore, we can write the equation: 68◦ +∠2 +∠3 = 180 ◦

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    - Therefore: ∠1 +∠2 = 180 ◦ - Substituting the value of∠1we found earlier: 70◦ +∠2 = 180 ◦ - Solving for∠2: ∠2 = 180◦ −70 ◦ = 110◦

    Notice that∠1and∠2are supplementary because they form a straight line. - Therefore: ∠1 +∠2 = 180 ◦ - Substituting the value of∠1we found earlier: 70◦ +∠2 = 180 ◦ - Solving for∠2: ∠2 = 180◦ −70 ◦ = 110◦

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    Revised Answer: The answer is D

    Now, substitute∠2back into the equation for the sum of angles in the triangle on the right: 68◦ + 110◦ +∠3 = 180 ◦ - Solving for∠3: ∠3 = 180◦ −68 ◦ −110 ◦ = 42◦ Therefore, the measure of∠3is42 ◦, which corresponds to option (D). Revised Answer: The answer is D. Analysis: The k...

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    Understanding the Problem: - Runner A starts at the origin and runs along the positive x-axis. -Runner B runs 3 times as fast as A and also starts from the origin. - The observer is standing one unit above the origin

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    Setting Up the Geometry: - Let’s denote the position of runner A at time t as A(t) and the position of runner B as B(t). - Since B runs 3 times as fast as A, if A runs a distancedin timet, B will run3din the same timet

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    Finding the Coordinates: - At any timet, the coordinates of A are(t,0). - The coordinates of B are(3t,0)

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    Calculating the Angles: - The angle of sight θ is the difference between the angles formed by the lines connecting the observer to A and the observer to B. - The angle formed by the line connecting the observer to A is α= tan −1 1 t . - The angle formed by the line connecting ...

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    - Using calculus, we can find the critical points by taking the derivative ofα−βwith respect totand setting it to zero

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Pith tools

Reviewed August 11, 2026 · model on record in the stance chip above.